Posts in category: Amazon AWS

This blog post is co-written with Nick Vargas and Anna Schreiber from Accenture.

Scheduling customer appointments is often a manual and labor-intensive process. You can utilize advances in self-service technology to automate appointment scheduling.

In this blog post, we show you how to build a self-service appointment scheduling solution built with Amazon Lex and Amazon Connect. This solution allows users to create appointments via Meta Messenger, and receive appointment confirmations through an SMS mobile message. It also provides a web-based dashboard so you can provide call to users with single-click button at the scheduled time.

Amazon Lex integrates with Meta messenger and can be used to enable chat conversations. Lex is a fully-managed artificial intelligence (AI) service with Natural language understanding (NLU) to design, build, test, and deploy conversational interfaces in applications.

Solution overview

The architecture diagram below shows a high-level overview of the interaction between different AWS components and services. The solution consists of these primary components: customer interaction using Meta messenger, appointment scheduling via SMS enabled by Lex and a customer outbound dialer from Connect. This outbound dialer makes it easy to create an outbound call to the customer from a simple UI interface.

This post uses the following sample bot conversation:

User: I would like to book an appointment.
Agent: What appointment can I get you? You can say Billing, General or Offers.
User: Billing
Agent: What’s your first name?
User: Sameer
Agent: What is your phone number with country code?
User: +10001234567
Agent: When should I schedule your Billing appointment?
User: Next week Tuesday
Agent: At what time should I schedule the Billing appointment?
User: 9:00 am
Agent: Sameer, 09:00 is available, should I go ahead and book your appointment?
User: Yes
Agent: Thanks Sameer, your appointment is confirmed for 09:00, and we have texted the details to your phone number.

For the scheduler and customer notification component, an AWS Lambda handler is used to process the scheduling request. The appointment information is then saved to a Amazon DynamoDB database. When the information is saved successfully, a notification is sent to the customer confirming the appointment details via SMS using Amazon Pinpoint.

A React.js application is created to display the saved customer appointments from the database in a calendar view format. This makes it easy for employees to identify the customers who need to be called. A call button from the calendar entry is clicked to initiate the call. This will immediately place an outbound call request to connect the customer with the employee using Amazon Connect.

Prerequisites

For this project, you should have the following prerequisites:

• Downloaded the code files from the GitHub repository.
  The repository contains:
  • The React app files, located under the UI
  • The Amazon Connect Contact Flows, located under backend/connect/contact_flows There are four contact flows for this demo with files names AgentWhisper, CustomerWaiting, InboundCall and OutboundCall.
  • A zip file for an Amazon Lex Bot, located in backend/lex directory with file name AppointmentSchedulerBot.zip.
• npm installed on your local machine. Refer how to install node.js and npm on your machine,

The deployment of this solution is automated where possible using CloudFormation, however, some configurations and steps in the deployment are manual.

Deploy the solution

To set up the required infrastructure for the appointment scheduler demo app in your AWS account, complete the following steps:

1. Sign in to the AWS Management Console.
2. Choose Launch Stack:
   
3. On the Create Stack page, under Specify template, choose Upload a template file.
4. Choose the AppointmentsSchedulerCFTemplate file that you downloaded from GitHub.
5. Choose Next.
6. For Stack name, enter a unique name for the stack, such as AppointmentSchedulerDemo.
   
7. Choose Next, and then choose Next on the Configure stack options page.
8. On the Review page, select I acknowledge that AWS CloudFormation might create IAM resources and choose Create.
   The stack generates the following resources:

• • The DynamoDB table AppointmentSchedulerTable
  • The Amazon Pinpoint app AppointmentSchedulerPinpointApp
  • Two AWS Identity and Access Management (IAM) policies:
    • AppointmentSchedulerPinpointPolicy
    • AppointmentSchedulerDynamoApiPolicy
  • Two IAM roles:
    • AppointmentsLambdaRole
    • OutboundContactLambdaRole
  • Two Lambda functions:
    • AppointmentScheduler
    • AppointmentSchedulerOutboundContact
  • The Amazon API Gateway instance Appointments
  • Amazon CloudFront distribution
  • The Amazon Simple Storage Service (Amazon S3) bucket appointment-scheduler-website

Configure the Amazon Pinpoint app

To configure the Amazon Pinpoint app, complete the following steps:

1. Go to the Pinpoint console.
2. Navigate to the AppointmentSchedulerPinpointApp deployed in above.
3. On the left menu under Settings click SMS and Voice.
4. Under Number settings click Request Phone Number.
5. Select your country of origin, choose Toll-free, and click Next, then Request.

The Amazon Lex bot for this post has one intent, MakeAppointment, which asks the user the series of questions in the preceding example to elicit the appointment type, date, time, name, and phone number of the customer.

AppointmentTypeValue is the only custom slot type for this bot and takes one of three values: Billing, General, or Offers. The Name, Phone, Date, and Time slots each use the built-in slot type provided by Amazon Lex.

Deploy the Amazon Lex bot

To deploy the bot, first import the Amazon Lex bot (AppointmentSchedulerLex.zip) into your account.

1. Sign in to the Amazon Lex V2 console.
2. If this is your first time using Amazon Lex, you will be shown the Welcome page, choose Create Bot.
3. When presented with the Create your bot page, scroll down to the bottom of the page, and select Cancel. If this is not your first-time using Amazon Lex, skip this step.
4. Choose Actions, then Import.
5. Enter AppointmentSchedulerBot for the bot’s name then choose the .zip archive to import.
6. Under IAM permissions, choose Create a role with basic Amazon Lex permissions.
7. Under COPPA, choose No.
8. Click Import.
9. Open the bot by clicking on the bot’s name.
10. Under Deployment on the left menu, click Aliases, select TestBotAlias and click English (US) under Languages. Choose the AppointmentScheduler Lambda function and click Save.
    
11. Under Bot Versions on the left menu, select Intents and at the bottom right-hand side of the page, click Build.
12. [Optional] Once the build has completed, click Test to test the bot using the window that appears on the right (click on the microphone icon to speak to your bot or type in the text box).

Set up an Amazon Connect Instance

To set up your Amazon Connect instance and contact flows, you complete the following steps:

1. Set up an Amazon Connect instance.
   1. Go to the Amazon Connect console.
   2. If this is the first time you have been to the Amazon Connect console, you will see the Welcome page, choose Get Started.
   3. If this is not the first time you are using Amazon Connect, click Add an instance.
   4. For Identity management, select Store users in Amazon Connect.
   5. For Access URL, type a unique name for your instance, for example, AppointmentSchedulerDemo, then choose Next.
   6. On the Add administrator page, add a new administrator account for Amazon Connect. Use this account to log in to your instance later using the unique access URL. Click Next step.
   7. On the next two pages – Telephony Options and Data storage – accept the default settings and choose Next step.
   8. On the Review and Create page, choose Create instance.
2. Add the Amazon Lex bots to your newly created Amazon Connect instance.
   1. Select the Instance Alias of the instance you just created.
   2. Choose Contact flows.
   3. Under Amazon Lex, use the drop-down to select the AppointmentSchedulerBot and the default alias.
      
   4. Choose + Add Amazon Lex Bot. If the name of your bot does not appear in the list, reload the page.
3. Log in to the instance and claim a phone number
   1. Click on the Login URL for your Connect Instance.
   2. Enter the Administrator credentials you entered upon creation of the instance. This will open the Connect Console.
   3. From the Dashboard, under Explore your channels of communication select View phone numbers on the right.
   4. Click Claim a number.
   5. Choose a Country and leave the default type of DID (Direct Inward Dialing), choose a Phone Number from the dropdown list, and click Next.
   6. Click Save.
4. Add the OutboundQueue
   1. From the navigation menu on the left, choose Queues from the Routing menu.
   2. Click Add New Queue.
   3. Name the Queue OutboundQueue, use the dropdown to set the Hours of operation to Basic Hours and use the dropdown for Outbound caller ID number to select the phone number you claimed earlier.
      
   4. Click Add new queue.
   5. From the navigation menu on the left, choose Routing Profiles from the Users menu.
   6. Click Basic Routing Profile. Under Routing profile queues, add OutboundQueue and click Save.
5. Add the phone number to BasicQueue
   1. From the navigation menu on the left, choose Queues from the Routing menu.
   2. Click on BasicQueue.
   3. In the Outbound caller ID number field, add the phone number that you claimed earlier.
   4. Click Save on the top right corner.
6. Import the InboundCall contact flow
   1. From the navigation menu on the left, choose Contact Flows from the Routing menu.
   2. Choose Create contact flow.
   3. On the right-hand side of the page, click on the down arrow and click Import flow (beta).
   4. Find the InboundCall file and choose Import.
      
   5. Click Publish.
7. Then, associate this flow with the phone number.
   1. From the navigation menu on the left, choose Phone Numbers from the Routing menu.
   2. Choose the phone number we created earlier.
   3. Under the Contact flow/IVR section, select the InboundCall flow.
      
   4. Click Save.
8. Import the AgentWhisper, CustomerWaiting, and OutboundCall contact flows
   1. From the left navigation menu, choose Contact Flows under Routing.
   2. Click Create Agent Whisper flow.
      
   3. On the right-hand side of the page, click on the down arrow and click Import flow (beta).
   4. Find the AgentWhisper file and choose Import.
   5. Click Publish.
   6. Navigate back to the Contact Flows list and click the down arrow next to Create contact flow.
   7. Click Create Customer Queue Flow.
      
   8. On the right-hand side of the page, click on the down arrow and click Import flow (beta).
   9. Find the  CustomerWaiting file and choose Import.
   10. Click Publish.
   11. Navigate back to the Contact Flows list and click the down arrow next to Create contact flow.
   12. Choose Create contact flow.
   13. On the right-hand side of the page, click on the down arrow and click Import flow (beta).
   14. Find the OutboundCall file from the GitHub repository you downloaded earlier and choose Import.
   15. Click Publish.

Edit Lambda Functions:

1. Go to the Lambda console.
2. Click on the AppointmentScheduler function.
3. Click on Configuration and Environment Variables from the left menu.
   
4. Click Edit. Replace the Value with your Pinpoint Project ID and Toll-free number. Click Save.
5. Return to the Lambda console and click on the AppointmentSchedulerOutboundContact function.
6. Repeat step 3 and 4, replacing the values for CONTACT_FLOW, INSTANCE_ID and QUEUE_ID with the correct values. Click Save once done.
   1. To find the contact flow ID, navigate to the OutboundCall Contact Flow in the Amazon Connect Console and click on the arrow next to Show additional flow information. The contact flow ID is the last value after contact-flow/.
      
   2. To find the instance ID, navigate to the Amazon Connect Console and click on your instance Alias. The instance ID is the last value in the Instance ARN after instance/.
      
   3. To find the queue ID, navigate to the OutboundQueue in the Amazon Connect Console and click on the arrow next to Show additional queue information. The contact flow ID is the last value after queue/.

The Lex Bots and Amazon Connect Instance are now ready to go. Next, we will deploy the UI.

Edit API Gateway route:

1. Go to the API Gateway console
2. Click the instance named Appointments
3. Under the resources section, click the POST method belonging to the /outcall resource.
4. Click Integration Request.
5. Then click the edit icon next to the right of the Lambda Function field. Then click the checkmark icon that have appeared to the right of the text field.
6. Click OK to add a permission to the Lambda function.

Deploy the UI:

1. Configure the UI before deployment
   1. In your preferred code editor, open the ui folder from the downloaded code files.
   2. Replace <your-api-ID> and <region> with your API ID (accessible under the ID column of the API Gateway Console) and the region of your deployed resources in the following lines: 103, 168, 310, 397, 438, 453.
      
   3. Replace <your-instance-name> with your Amazon Connect instance name on line 172 and 402.
      
   4. [Optional] add an app logo in the index.js file, line 331:
      
      In the index.html file, line 5:
      
   5. In a terminal, navigate to the ui folder of the downloaded project.
   6. Run npm install. This will take a few minutes to complete.
   7. Run npm run-script build. This will generate a build folder in the ui directory.
2. Add the code files to the S3 bucket:
   1. Go to the S3 Console.
   2. Search for the bucket deployed with the CloudFormation Stack, appointment-scheduler-website-<random_id>.
   3. Drag and drop the contents of the build folder in the ui directory created in the last step into the bucket.
   4. Click Upload.
      
      You should now be able to access the application from the CloudFront Distribution.
3. Add the CloudFront Distribution as an approved origin.
   1. 1. Go to the Amazon Connect console.
      2. Select the Instance Alias of the instance to which to add the bot.
      3. Choose Approved origins.
      4. Click + Add origin and enter the URL of your CloudFront Distribution.
      5. Click Add.
4. Now navigate to your CloudFront Distribution URL plus index.html. (e.g., https:// <DistributionDomainName>.cloudfront.net/index.html)

Clean up

One finished with this solution, make sure to clean up your AWS environment as to not incur unwanted charges.

1. Go to the S3 console, empty your bucket created by the CloudFormation template (appointment-scheduler-website).
2. Go to the CloudFormation console, delete your stack. Ensure that all resources associated with this stack were deleted successfully.
3. Go to the Amazon Connect console, delete your instance.
4. Go to the Amazon Lex console, delete the bot you created.

Conclusion

For this blog, Accenture and AWS collaborated to develop a machine learning solution that highlights the use of AWS services to build an automated appointment scheduler. This solution demonstrates how easy it is to build an appointment scheduling solution in AWS. Amazon Lex’s ability to support third-party messaging services such as Meta messenger extends the potential reach of the solution across multiple channels. Customer notification via SMS is implemented with minimal effort using Amazon Pinpoint. With Amazon Connect, an outbound dialer is seamlessly integrated with the calendar view web application enabling employees to immediately connect to customers with a simple click-to-call button.

You can accelerate innovation with the Accenture AWS Business Group (AABG). You can learn from the resources, technical expertise, and industry knowledge of two leading innovators, helping you accelerate the pace of innovation to deliver disruptive products and services. The AABG helps customers ideate and innovate cloud solutions for customers through rapid prototype development. Connect with our team a accentureaws@amazon.com to learn and accelerate how to use machine learning in your products and services.

About the Authors

Sameer Goel is a Sr. Solutions Architect in the Netherlands, who drives customer success by building prototypes on cutting-edge initiatives. Prior to joining AWS, Sameer graduated with a master’s degree from Boston, with a concentration in data science. He enjoys building and experimenting with AI/ML projects on Raspberry Pi.

Nick Vargas is a Manager and Technology Architect at Accenture. He leads the project delivery for a rapid prototyping team within the Accenture AWS Business Group (AABG). He enjoys his morning walks with his dog Bingo, traveling, going to the beach, and hiking.

Anna Schreiber is part of a prototyping team within Accenture’s AWS Business Group (AABG). As a Senior AWS Developer, she has worked on several high-profile proof of concepts that help bring the client’s vision to life. When not working, she enjoys cooking, crafting, and playing fetch with her corgi Gimli.

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This post is co-written with Sowmya Manusani, Sr. Staff Machine Learning Engineer at Zendesk

Zendesk is a SaaS company that builds support, sales, and customer engagement software for everyone, with simplicity as the foundation. It thrives on making over 170,000 companies worldwide serve their hundreds of millions of customers efficiently. The Machine Learning team at Zendcaesk is responsible for enhancing Customer Experience teams to achieve their best. By combining the power of data and people, Zendesk delivers intelligent products that make their customers more productive by automating manual work.

Zendesk has been building ML products since 2015, including Answer Bot, Satisfaction Prediction, Content Cues, Suggested Macros, and many more. In the last few years, with the growth in deep learning, especially in NLP, they saw a lot of opportunity to automate workflows and assist agents in supporting their customers with Zendesk solutions. Zendesk currently use TensorFlow and PyTorch to build deep learning models.

Customers like Zendesk have built successful, high-scale software as a service (SaaS) businesses on Amazon Web Services (AWS). A key driver for a successful SaaS business model is the ability to apply multi-tenancy in the application and infrastructure. This enables cost and operational efficiencies because the application only needs to be built once, but it can be used many times and the infrastructure can be shared. We see many customers build secure, cost-efficient, multi-tenant systems on AWS at all layers of the stack, from compute, storage, database, to networking, and now we’re seeing customers needing to apply it to machine learning (ML).

Making the difficult tradeoff between model reuse and hyper-personalization

Multi-tenancy for SaaS businesses typically means that a single application is reused between many users (SaaS customers). This creates cost efficiencies and lowers operational overhead. However, machine learning models sometimes need to be personalized to a high degree of specificity (hyper-personalized) to make accurate predictions. This means the “build once, use many times” SaaS paradigm can’t always be applied to ML if models have specificity. Take for example the use case of customer support platforms. The language that users include in a support ticket varies depending on if it’s a ride share issue (“ride took too long”) or a clothing purchase issue (“discoloration when washed”). In this use case, improving the accuracy of predicting the best remediation action may require training a natural language processing (NLP) model on a dataset specific to a business domain or an industry vertical. Zendesk face exactly this challenge when trying to leverage ML in their solutions. They needed to create thousands of highly customized ML models, each tailored for a specific customer. To solve this challenge of deploying thousands of models, cost effectively, Zendesk turned to Amazon SageMaker.

In this post, we show how to use some of the newer features of Amazon SageMaker, a fully managed machine learning service, to build a multi-tenant ML inference capability. We also share a real-world example of how Zendesk successfully achieved the same outcome by deploying a happy medium between being able to support hyper-personalization in their ML models and the cost-efficient, shared use of infrastructure using SageMaker multi-model endpoints (MME).

SageMaker multi-model endpoints

SageMaker multi-model endpoints enable you to deploy multiple models behind a single inference endpoint that may contain one or more instances. Each instance is designed to load and serve multiple models up to its memory and CPU capacity. With this architecture, a SaaS business can break the linearly increasing cost of hosting multiple models and achieve reuse of infrastructure consistent with the multi-tenancy model applied elsewhere in the application stack.

The following diagram illustrates the architecture of a SageMaker multi-model endpoint.

The SageMaker multi-model endpoint dynamically loads models from Amazon Simple Storage Service (Amazon S3) when invoked, instead of downloading all the models when the endpoint is first created. As a result, an initial invocation to a model might see higher inference latency than the subsequent inferences, which are completed with low latency. If the model is already loaded on the container when invoked, then the download step is skipped and the model returns the inferences with low latency. For example, assume you have a model that is only used a few times a day. It is automatically loaded on demand, while frequently accessed models are retained in memory and invoked with consistently low latency.

Let’s take a closer look at how Zendesk used SageMaker MME to achieve cost-effective, hyper-scale ML deployment with their Suggested Macros ML feature.

Why Zendesk built hyper-personalized models

Zendesk’s customers are spread globally in different industry verticals with different support ticket semantics. Therefore, to serve their customers best, they often have to build personalized models that are trained on customer-specific support ticket data to correctly identify intent, macros, and more.

In October 2021, they released a new NLP ML feature, Suggested Macros, which recommends macros (predefined actions) based on thousands of customer-specific model predictions. Zendesk’s ML team built a TensorFlow-based NLP classifier model trained from the previous history of ticket content and macros per customer. With these models available, a macro prediction is recommended whenever an agent views the ticket (as shown in the following screenshot), which assists the agent in serving customers quickly. Because macros are specific to customers, Zendesk needs customer-specific models to serve accurate predictions.

Under the hood of Zendesk’s Suggested Macros

Suggested Macros models are NLP-based neural nets that are around 7–15 MB in size. The main challenge is to put thousands of these models in production with cost-efficient, reliable, and scalable solutions.

Each model has different traffic patterns, with a minimum of two requests per second and a peak of hundreds of requests per second, serving millions of predictions per day with a model latency of approximately 100 milliseconds when the model is available in memory. SageMaker endpoints are deployed in multiple AWS Regions, serving thousands of requests per minute per endpoint.

With its ability to host multiple models on a single endpoint, SageMaker helped Zendesk reduce deployment overhead and create a cost-effective solution when compared to deploying a single-model endpoint per customer. The tradeoff here is less control on per-model management; however, this is an area where Zendesk is collaborating with AWS to improve multi-model endpoints.

One of the SageMaker multi-model features is lazy loading of models, that is, models are loaded into memory when invoked for the first time. This is to optimize memory utilization; however, it causes response time spikes on first load, which can be seen as a cold start problem. For Suggested Macros, this was a challenge; however, Zendesk overcame this by implementing a preloading functionality on top of the SageMaker endpoint provisioning to load the models into memory before serving production traffic. Secondly, MME unloads infrequently used models from memory, so to achieve consistent low latency on all the models and avoid “noisy neighbors” impacting other less active models, Zendesk is collaborating with AWS to add new features, discussed later in the post, to enable more explicit per-model management. Additionally, as an interim solution, Zendesk has right-sized the MME fleet to minimize too many models unloading. With this, Zendesk is able to serve predictions to all their customers with low latency, around 100 milliseconds, and still achieve 90% cost savings when compared to dedicated endpoints.

On right-sizing MME, Zendesk observed during load testing that having a higher number of smaller instances (bias on horizontal scaling) behind MME was a better choice than having fewer larger memory instances (vertical scaling). Zendesk observed that bin packing too many models (beyond 500 TensorFlow models in their case) on a single large memory instance didn’t work well because memory is not the only resource on an instance that can be a bottleneck. More specifically, they observed that TensorFlow spawned multiple threads (3 x total instance vCPUs) per model, so loading over 500 models on a single instance caused kernel level limits to be breached on the max number of threads that could be spawned on an instance. Another issue with using fewer, larger instances occurred when Zendesk experienced throttling (as a safety mechanism) on some instances behind MME because the unique model invocation per second rate exceeded what the Multi Model Server (MMS) on a single instance could safely handle without browning out the instance. This was another issue that was resolved with the use of more and smaller instances.

From the observability perspective, which is a crucial component of any production application, Amazon CloudWatch metrics like invocations, CPU, memory utilization, and multi model-specific metrics like loaded models in memory, model loading time, model load wait time, and model cache hit are informative. Specifically, the breakdown of model latency helped Zendesk understand the cold start problem and its impact.

Under the hood of MME auto scaling

Behind each multi-model endpoint, there are model hosting instances, as depicted in the following diagram. These instances load and evict multiple models to and from memory based on the traffic patterns to the models.

SageMaker continues to route inference requests for a model to the instance where the model is already loaded such that the requests are served from cached model copy (see the following diagram, which shows the request path for the first prediction request vs. the cached prediction request path). However, if the model receives many invocation requests, and there are additional instances for the multi-model endpoint, SageMaker routes some requests to another instance to accommodate the increase. To take advantage of automated model scaling in SageMaker, make sure you have instance auto scaling set up to provision additional instance capacity. Set up your endpoint-level scaling policy with either custom parameters or invocations per minute (recommended) to add more instances to the endpoint fleet.

Use cases best suited for MME

SageMaker multi-model endpoints are well suited for hosting a large number of similar models that you can serve through a shared serving container and don’t need to access all the models at the same time. MME is best suited for models that are similar in size and invocation latencies. Some variation in model size is acceptable; for example, Zendesk’s models range from 10–50 Mb, which works fine, but variations in size that are a factor of 10, 50, or 100 times greater aren’t suitable. Larger models may cause a higher number of loads and unloads of smaller models to accommodate sufficient memory space, which can result in added latency on the endpoint. Differences in performance characteristics of larger models could also consume resources like CPU unevenly, which could impact other models on the instance.

MME is also designed for co-hosting models that use the same ML framework because they use the shared container to load multiple models. Therefore, if you have a mix of ML frameworks in your model fleet (such as PyTorch and TensorFlow), SageMaker dedicated endpoints or multi-container hosting is a better choice. Finally, MME is suited for applications that can tolerate an occasional cold start latency penalty because infrequently used models can be off-loaded in favor of frequently invoked models. If you have a long tail of infrequently accessed models, a multi-model endpoint can efficiently serve this traffic and enable significant cost savings.

Summary

In this post, you learned how SaaS and multi-tenancy relate to ML and how SageMaker multi-model endpoints enable multi-tenancy and cost-efficiency for ML inference. You learned about Zendesk’s multi-tenanted use case of per-customer ML models and how they hosted thousands of ML models in SageMaker MME for their Suggested Macros feature and achieved 90% cost savings on inference when compared to dedicated endpoints. Hyper-personalization use cases can require thousands of ML models, and MME is a cost-effective choice for this use case. We will continue to make enhancements in MME to enable you to host models with low latency and with more granular controls for each personalized model. To get started with MME, see Host multiple models in one container behind one endpoint.

About the Authors

Syed Jaffry is a Sr. Solutions Architect with AWS. He works with a range of companies from mid-sized organizations to large enterprises, financial services to ISVs, in helping them build and operate secure, resilient, scalable, and high performance applications in the cloud.

Sowmya Manusani is a Senior Staff Machine Learning Engineer at Zendesk. She works on productionalizing NLP-based Machine Learning features that focus on improving Agent productivity for thousands of Zendesk Enterprise customers. She has experience with building automated training pipelines for thousands of personalized models and serving them using secure, resilient, scalable, and high-performance applications. In her free time, she likes to solve puzzles and try painting.

Saurabh Trikande is a Senior Product Manager for Amazon SageMaker Inference. He is passionate about working with customers and making machine learning more accessible. In his spare time, Saurabh enjoys hiking, learning about innovative technologies, following TechCrunch and spending time with his family.

Deepti Ragha is a Software Development Engineer in the Amazon SageMaker team. Her current work focuses on building features to host machine.

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Mantium is a global cloud platform provider for building AI applications and managing them at scale. Mantium’s end-to-end development platform enables enterprises and businesses of all sizes to build AI applications and automation faster and easier than what has been traditionally possible. With Mantium, technical and non-technical teams can prototype, develop, test, and deploy AI applications, all with a low-code approach. Through automatic logging, monitoring, and safety features, Mantium also releases software and DevOps engineers from spending their time reinventing the wheel. At a high level, Mantium delivers:

• State-of-the-art AI – Experiment and develop with an extensive selection of open-source and private large language models with a simple UI or API.
• AI process automation – Easily build AI-driven applications with a growing library of integrations and Mantium’s graphical AI Builder.
• Rapid deployment – Shorten the production timeline from months to weeks or even days with one-click deployment. This feature turns AI applications into shareable web apps with one click.
• Safety and regulation – Ensure safety and compliance with governance policies and support for human-in-the-loop processes.

With the Mantium AI Builder, you can develop sophisticated workflows that integrate external APIs, logic operations, and AI models. The following screenshot shows an example of the Mantium AI app, which chains together a Twilio input, governance policy, AI block (which can rely on an open-source model like GPT-J) and Twilio output.

To support this app, Mantium provides comprehensive and uniform access to not only model APIs from AI providers like Open AI, Co:here, and AI21, but also state-of-the-art open source models. At Mantium, we believe that anyone should be able to build modern AI applications that they own, end-to-end, and we support this by providing no-code and low-code access to performance-optimized open-source models.

For example, one of Mantium’s core open-source models is GPT-J, a state-of-the-art natural language processing (NLP) model developed by EleutherAI. With 6 billion parameters, GPT-J is one of the largest and best-performing open-source text generation models. Mantium users can integrate GPT-J into their AI applications via Mantium’s AI Builder. In the case of GPT-J, this involves specifying a prompt (a natural language representation of what the model should do) and configuring some optional parameters.

For example, the following screenshot shows an abbreviated demonstration of a sentiment analysis prompt that produces explanations and sentiment predictions. In this example, the author wrote that the “food was wonderful” and that their “service was extraordinary.” Therefore, this text expresses positive sentiment.

However, one challenge with open-source models is that they’re rarely designed for production-grade performance. In the case of large models like GPT-J, this can make production deployment impractical and even infeasible, depending on the use case.

To ensure that our users have access to best-in-class performance, we’re always looking for ways to decrease the latency of our core models. In this post, we describe the results of an inference optimization experiment in which we use DeepSpeed’s inference engine to increase GPT-J’s inference speed by approximately 116%. We also describe how we have deployed the Hugging Face Transformers implementation of GPT-J with DeepSpeed in our Amazon SageMaker inference endpoints.

Overview of the GPT-J model

GPT-J is a generative pretrained (GPT) language model and, in terms of its architecture, it’s comparable to popular, private, large language models like Open AI’s GPT-3. As noted earlier, it consists of approximately 6 billion parameters and 28 layers, which consist of a feedforward block and a self-attention block. When it was first released, GPT-J was one of the first large language models to use rotary embeddings, a new position encoding strategy that unifies absolute and relative position encoders. It also employs an innovative parallelization strategy where dense and feedforward layers are combined in a single layer, which minimizes communication overhead.

Although GPT-J might not quite qualify as large by today’s standards—large models typically consist of more than 100 billion parameters—it’s still impressively performant, and with some prompt engineering or minimal fine-tuning, you can use it to solve many problems. Furthermore, its relatively modest size means that you can deploy it more rapidly and at a much lower cost than larger models.

That said, GPT-J is still pretty big. For example, training GPT-J in FP32 with full weight updates and the Adam optimizer requires over 200 GB memory: 24 GB for the model parameters, 24 GB for the gradients, 24 GB for Adam’s squared gradients, 24 GB for the optimizer states, and the additional memory requirements for loading training batches and storing activations. Of course, training in FP16 reduces these memory requirements almost by half, but a memory footprint of over 100 GB still necessitates innovative training strategies. For instance, in collaboration with SageMaker, Mantium’s NLP team developed a workflow for training (fine-tuning) GPT-J using the SageMaker distributed model parallel library.

In contrast, serving GPT-J for inference has much lower memory requirements—in FP16, model weights occupy less than 13 GB, which means that inference can easily be conducted on a single 16 GB GPU. However, inference with out-of-the-box implementations of GPT-J, such as the Hugging Face Transformers implementation that we use, is relatively slow. To support use cases that require highly responsive text-generation, we’ve focused on reducing GPT-J’s inference latency.

Response latency challenges of GPT-J

Response latency is a core obstacle for the generative pretrained transformers (GPTs) such as GPT-J that power modern text generation. GPT models generate text through sequences of inference steps. At each inference step, the model is given text as input, and, conditional on this input, it samples a word from its vocabulary to append to the text. For example, given the sequence of tokens “I need an umbrella because it’s,” a high-likelihood next token might be “raining.” However, it could also be “sunny” or “bound,” which could be the first step toward a text sequence like “I need an umbrella because it’s bound to start raining.”

Scenarios like this raise some interesting challenges for deploying GPT models because real-world use cases might involve tens, hundreds, or even thousands of inference steps. For example, generating a 1,000-token response requires 1,000 inference steps! Accordingly, although a model might offer inference speeds that seem fast enough in isolation, it’s easy for latency to reach untenable levels when long texts are generated. We observed an average latency of 280 milliseconds per inference step on a V100 GPU. This might seem fast for a 6.7 billion parameter model, but with such latencies, it takes approximately 30 seconds to generate a 500-token response, which isn’t ideal from a user experience perspective.

Optimizing inference speeds with DeepSpeed Inference

DeepSpeed is an open-source deep-learning optimization library developed by Microsoft. Although it primarily focuses on optimizing of training large models, DeepSpeed also provides an inference optimization framework that supports a select set of models, including BERT, Megatron, GPT-Neo, GPT2, and GPT-J. DeepSpeed Inference facilitates high-performance inference with large Transformer-based architectures through a combination of model parallelism, inference-optimized CUDA kernels, and quantization.

To boost inference speed with GPT-J, we use DeepSpeed’s inference engine to inject optimized CUDA kernels into the Hugging Face Transformers GPT-J implementation.

To evaluate the speed benefits of DeepSpeed’s inference engine, we conducted a series of latency tests in which we timed GPT-J under various configurations. Specifically, we varied whether or not DeepSpeed was used, hardware, output sequence length, and input sequence length. We focused on both output and input sequence length, because they both affect inference speed. To generate an output sequence of 50 tokens, the model must perform 50 inference steps. Furthermore, the time required to perform an inference step depends on the size of the input sequence—larger inputs require more processing time. Although the effect of output sequence size is much larger than the effect of input sequence size, it’s still necessary to account for both factors.

In our experiment, we used the following design:

• DeepSpeed inference engine – On, off
• Hardware – T4 (ml.g4dn.2xlarge), V100 (ml.p3.2xlarge)
• Input sequence length – 50, 200, 500, 1000
• Output sequence length – 50, 100, 150, 200

In total, this design has 64 combinations of these four factors, and for each combination, we ran 20 latency tests. Each test was run on a pre-initialized SageMaker inference endpoint, ensuring that our latency tests reflect production times, including API exchanges and preprocessing.

Our tests demonstrate that DeepSpeed’s GPT-J inference engine is substantially faster than the baseline Hugging Face Transformers PyTorch implementation. The following figure illustrates the mean text generation latencies for GPT-J with and without DeepSpeed acceleration on ml.g4dn.2xlarge and ml.p3.2xlarge SageMaker inference endpoints.

On the ml.g4dn.2xlarge instance, which is equipped with a 16 GB NVIDIA T4 GPU, we observed a mean latency reduction of approximately 24% [Standard Deviation (SD) = 0.05]. This corresponded to an increase from a mean 12.5 (SD = 0.91) tokens per second to a mean 16.5 (SD = 2.13) tokens per second. Notably, DeepSpeed’s acceleration effect was even stronger on the ml.p3.2xlarge instance, which is equipped with an NVIDIA V100 GPU. On that hardware, we observed a 53% (SD = .07) mean latency reduction. In terms of tokens per second, this corresponded to an increase from a mean 21.9 (SD = 1.97) tokens per second to a mean 47.5 (SD = 5.8) tokens per second.

We also observed that the acceleration offered by DeepSpeed attenuated slightly on both hardware configurations as the size of the input sequences grew. However, across all conditions, inference with DeepSpeed’s GPT-J optimizations was still substantially faster than the baseline. For example, on the g4dn instance, the maximum and minimum latency reductions were 31% (input sequence size = 50) and 15% (input sequence size = 1000), respectively. And on the p3 instance, the maximum and minimum latency reductions were 62% (input sequence size = 50) and 40% (input sequence size = 1000), respectively.

Deploying GPT-J with DeepSpeed on a SageMaker inference endpoint

In addition to dramatically increasing text generation speeds for GPT-J, DeepSpeed’s inference engine is simple to integrate into a SageMaker inference endpoint. Before adding DeepSpeed to our inference stack, our endpoints were running on a custom Docker image based on an official PyTorch image. SageMaker makes it very easy to deploy custom inference endpoints, and integrating DeepSpeed was as simple as including the dependency and writing a few lines of code. The open-sourced guide to the deployment workflow to deploy GPT-J with DeepSpeed is available on GitHub.

Conclusion

Mantium is dedicated to leading innovation so that everyone can quickly build with AI. From AI-driven process automation to stringent safety and compliance settings, our complete platform provides all the tools necessary to develop and manage robust, responsible AI applications at scale and lowers the barrier to entry. SageMaker helps companies like Mantium get to market quickly.

To learn how Mantium can help you build complex AI-driven workflows for your organization, visit www.mantiumai.com.

About the authors

Joe Hoover is a Senior Applied Scientist on Mantium’s AI R&D team. He is passionate about developing models, methods, and infrastructure that help people solve real-world problems with cutting-edge NLP systems. In his spare time, he enjoys backpacking, gardening, cooking, and hanging out with his family.

Dhawal Patel is a Principal Machine Learning Architect at AWS. He has worked with organizations ranging from large enterprises to mid-sized startups on problems related to distributed computing, and Artificial Intelligence. He focuses on Deep learning including NLP and Computer Vision domains. He helps customers achieve high performance model inference on SageMaker.

Sunil Padmanabhan is a Startup Solutions Architect at AWS. As a former startup founder and CTO, he is passionate about machine learning and focuses on helping startups leverage AI/ML for their business outcomes and design and deploy ML/AI solutions at scale.

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Amazon SageMaker Data Wrangler is a purpose-built data aggregation and preparation tool for machine learning (ML). It allows you to use a visual interface to access data and perform exploratory data analysis (EDA) and feature engineering. The EDA feature comes with built-in data analysis capabilities for charts (such as scatter plot or histogram) and time-saving model analysis capabilities such as feature importance, target leakage, and model explainability. The feature engineering capability has over 300 built-in transforms and can perform custom transformations using either Python, PySpark, or Spark SQL runtime.

For custom visualizations and transforms, Data Wrangler now provides example code snippets for common types of visualizations and transforms. In this post, we demonstrate how to use these code snippets to quickstart your EDA in Data Wrangler.

Solution overview

At the time of this writing, you can import datasets into Data Wrangler from Amazon Simple Storage Service (Amazon S3), Amazon Athena, Amazon Redshift, Databricks, and Snowflake. For this post, we use Amazon S3 to store the 2014 Amazon reviews dataset. The following is a sample of the dataset:

{ "reviewerID": "A2SUAM1J3GNN3B", "asin": "0000013714", "reviewerName": "J. McDonald", "helpful": [2, 3], "reviewText": "I bought this for my husband who plays the piano. He is having a wonderful time playing these old hymns. The music is sometimes hard to read because we think the book was published for singing from more than playing from. Great purchase though!", "overall": 5.0, "summary": "Heavenly Highway Hymns", "unixReviewTime": 1252800000, "reviewTime": "09 13, 2009" } 

In this post, we perform EDA using three columns—asin, reviewTime, and overall—which map to the product ID, review time date, and the overall review score, respectively. We use this data to visualize dynamics for the number of reviews across months and years.

Using example Code Snippet for EDA in Data Wrangler

To start performing EDA in Data Wrangler, complete the following steps:

1. Download the Digital Music reviews dataset JSON and upload it to Amazon S3.
   We use this as the raw dataset for the EDA.
2. Open Amazon SageMaker Studio and create a new Data Wrangler flow and import the dataset from Amazon S3.
   

   This dataset has nine columns, but we only use three: asin, reviewTime, and overall. We need to drop the other six columns.

3. Create a custom transform and choose Python (PySpark).
4. Expand Search example snippets and choose Drop all columns except several.
5. Enter the provided snippet into your custom transform and follow the directions to modify the code.

   # Specify the subset of columns to keep
   cols = ["asin", "reviewTime", "overall"]
   
   cols_to_drop = set(df.columns).difference(cols) 
   df = df.drop(*cols_to_drop)

   Now that we have all the columns we need, let’s filter the data down to only keep reviews between 2000–2020.

6. Use the Filter timestamp outside range snippet to drop the data before year 2000 and after 2020:

   from pyspark.sql.functions import col
   from datetime import datetime
   
   # specify the start and the stop timestamp
   timestamp_start = datetime.strptime("2000-01-01 12:00:00", "%Y-%m-%d %H:%M:%S")
   timestamp_stop = datetime.strptime("2020-01-01 12:00:00", "%Y-%m-%d %H:%M:%S")
   
   df = df.filter(col("reviewTime").between(timestamp_start, timestamp_stop))

   Next, we extract the year and month from the reviewTime column.

7. Use the Featurize date/time transform.
8. For Extract columns, choose year and month.
   

   Next, we want to aggregate the number of reviews by year and month that we created in the previous step.

9. Use the Compute statistics in groups snippet:

   # Table is available as variable `df`
   from pyspark.sql.functions import sum, avg, max, min, mean, count
   
   # Provide the list of columns defining groups
   groupby_cols = ["reviewTime_year", "reviewTime_month"]
   
   # Specify the map of aggregate function to the list of colums
   # aggregates to use: sum, avg, max, min, mean, count
   aggregate_map = {count: ["overall"]}
   
   all_aggregates = []
   for a, cols in aggregate_map.items():
       all_aggregates += [a(col) for col in cols]
   
   df = df.groupBy(groupby_cols).agg(*all_aggregates)
10. Rename the aggregation of the previous step from count(overall) to reviews_num by choosing Manage Columns and the Rename column transform.
    Finally, we want to create a heatmap to visualize the distribution of reviews by year and by month.
11. On the analysis tab, choose Custom visualization.
12. Expand Search for snippet and choose Heatmap on the drop-down menu.
13. Enter the provided snippet into your custom visualization:

    # Table is available as variable `df`
    # Table is available as variable `df`
    import altair as alt
    
    # Takes first 1000 records of the Dataframe
    df = df.head(1000)  
    
    chart = (
        alt.Chart(df)
        .mark_rect()
        .encode(
            # Specify the column names for X and Y axis,
            # Both should have discrete values: ordinal (:O) or nominal (:N)
            x= "reviewTime_year:O",
            y="reviewTime_month:O",
            # Color can be both discrete (:O, :N) and quantitative (:Q)
            color="reviews_num:Q",
        )
        .interactive()
    )

    We get the following visualization.

    
    If you want to enhance the heatmap further, you can slice the data to only show reviews prior to 2011. These are hard to identify in the heatmap we just created due to large volumes of reviews since 2012.

14. Add one line of code to your custom visualization:

    # Table is available as variable `df`
    import altair as alt
    
    df = df[df.reviewTime_year < 2011]
    # Takes first 1000 records of the Dataframe
    df = df.head(1000)  
    
    chart = (
        alt.Chart(df)
        .mark_rect()
        .encode(
            # Specify the column names for X and Y axis,
            # Both should have discrete values: ordinal (:O) or nominal (:N)
            x= "reviewTime_year:O",
            y="reviewTime_month:O",
            # Color can be both discrete (:O, :N) and quantitative (:Q)
            color="reviews_num:Q",
        )
        .interactive()
    )

We get the following heatmap.

Now the heatmap reflects the reviews prior to 2011 more visibly: we can observe the seasonal effects (the end of the year brings more purchases and therefore more reviews) and can identify anomalous months, such as October 2003 and March 2005. It’s worth investigating further to determine the cause of those anomalies.

Conclusion

Data Wrangler is a purpose-built data aggregation and preparation tool for ML. In this post, we demonstrated how to perform EDA and transform your data quickly using code snippets provided by Data Wrangler. You just need to find a snippet, enter the code, and adjust the parameters to match your dataset. You can continue to iterate on your script to create more complex visualizations and transforms.
To learn more about Data Wrangler, refer to Create and Use a Data Wrangler Flow.

About the Authors

Nikita Ivkin is an Applied Scientist, Amazon SageMaker Data Wrangler.

Haider Naqvi is a Solutions Architect at AWS. He has extensive software development and enterprise architecture experience. He focuses on enabling customers to achieve business outcomes with AWS. He is based out of New York.

Harish Rajagopalan is a Senior Solutions Architect at Amazon Web Services. Harish works with enterprise customers and helps them with their cloud journey.

James Wu is a Senior Customer Solutions Manager at AWS, based in Dallas, TX. He works with customers to accelerate their cloud journey and fast-track their business value realization. In addition to that, James is also passionate about developing and scaling large AI/ ML solutions across various domains. Prior to joining AWS, he led a multi-discipline innovation technology team with ML engineers and software developers for a top global firm in the market and advertising industry.

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Customers in industries like consumer packaged goods, manufacturing, and retail are always looking for ways to empower their operational processes by enriching them with insights and analytics generated from data. Tasks like sales forecasting directly affect operations such as raw material planning, procurement, manufacturing, distribution, and inbound/outbound logistics, and it can have many levels of impact, from a single warehouse all the way to large-scale production facilities.

Sales representatives and managers use historical sales data to make informed predictions about future sales trends. Customers use SAP ERP Central Component (ECC) to manage planning for the manufacturing, sale, and distribution of goods. The sales and distribution (SD) module within SAP ECC helps manage sales orders. SAP systems are the primary source of historical sales data.

Sales representatives and managers have the domain knowledge and in-depth understanding of their sales data. However, they lack data science and programming skills to create machine learning (ML) models that can generate sales forecasts. They seek intuitive, simple-to-use tools to create ML models without writing a single line of code.

To help organizations achieve the agility and effectiveness that business analysts seek, we introduced Amazon SageMaker Canvas, a no-code ML solution that helps you accelerate delivery of ML solutions down to hours or days. Canvas enables analysts to easily use available data in data lakes, data warehouses, and operational data stores; build ML models; and use them to make predictions interactively and for batch scoring on bulk datasets—all without writing a single line of code.

In this post, we show how to bring sales order data from SAP ECC to generate sales forecasts using an ML model built using Canvas.

Solution overview

To generate sales forecasts using SAP sales data, we need the collaboration of two personas: data engineers and business analysts (sales representatives and managers). Data engineers are responsible for configuring the data export from the SAP system to Amazon Simple Storage Service (Amazon S3) using Amazon AppFlow, which business analysts can then run either on-demand or automatically (schedule-based) to refresh SAP data in the S3 bucket. Business analysts are then responsible for generating forecasts with the exported data using Canvas. The following diagram illustrates this workflow.

For this post, we use SAP NetWeaver Enterprise Procurement Model (EPM) for the sample data. EPM is generally used for demonstration and testing purposes in SAP. It uses common business process model and follows the business object (BO) paradigm to support a well-defined business logic. We used the SAP transaction SEPM_DG (data generator) to generate around 80,000 historical sales orders and created a HANA CDS view to aggregate the data by product ID, sales date, and city, as shown in the following code:

@AbapCatalog.sqlViewName: 'ZCDS_EPM_VIEW'
@AbapCatalog.compiler.compareFilter: true
@AbapCatalog.preserveKey: true
@AccessControl.authorizationCheck: #CHECK
@EndUserText.label: 'Sagemaker canvas sales order'
@OData.publish: true 
define view ZCDS_EPM as select from epm_v_sales_data as sd
inner join epm_v_bp as bp
    on sd.bp_id = bp.bp_id  {
    key sd.product_id as productid,
    bp.city,
    concat( cast(
         Concat(
            Concat(
                Concat(substring(cast (sd.created_at as abap.char( 30 )), 1, 4), '-'),
                Concat(substring(cast (sd.created_at as abap.char( 30 )), 5, 2), '-')
         ),
      Substring(cast (sd.created_at as abap.char( 30 )), 7, 2)
         )
      as char10 preserving type),' 00:00:00') as saledate,
    cast(sum(sd.gross_amount) as abap.dec( 15, 3 )) as totalsales 
}
group by sd.product_id,sd.created_at, bp.city

In the next section, we expose this view using SAP OData services as ABAP structure, which allows us to extract the data with Amazon AppFlow.

The following table shows the representative historical sales data from SAP, which we use in this post.

The data file is daily frequency historical data. It has four columns (productid, saledate, city, and totalsales). We use Canvas to build an ML model that is used to forecast totalsales for productid in a particular city.

This post has been organized to show the activities and responsibilities for both data engineers and business analysts to generate product sales forecasts.

Data engineer: Extract, transform, and load the dataset from SAP to Amazon S3 with Amazon AppFlow

The first task you perform as a data engineer is to run an extract, transform, and load (ETL) job on historical sales data from SAP ECC to an S3 bucket, which the business analyst uses as the source dataset for their forecasting model. For this, we use Amazon AppFlow, because it provides an out-of-the-box SAP OData Connector for ETL (as shown in the following diagram), with a simple UI to set up everything needed to configure the connection from the SAP ECC to the S3 bucket.

Prerequisites

The following are requirements to integrate Amazon AppFlow with SAP:

• SAP NetWeaver Stack version 7.40 SP02 or above
• Catalog service (OData v2.0/v2.0) enabled in SAP Gateway for service discovery
• Support for client-side pagination and query options for SAP OData Service
• HTTPS enabled connection to SAP

Authentication

Amazon AppFlow supports two authentication mechanisms to connect to SAP:

• Basic – Authenticates using SAP OData user name and password.
• OAuth 2.0 – Uses OAuth 2.0 configuration with an identity provider. OAuth 2.0 must be enabled for OData v2.0/v2.0 services.

Connection

Amazon AppFlow can connect to SAP ECC using a public SAP OData interface or a private connection. A private connection improves data privacy and security by transferring data through the private AWS network instead of the public internet. A private connection uses the VPC endpoint service for the SAP OData instance running in a VPC. The VPC endpoint service must have the Amazon AppFlow service principal appflow.amazonaws.com as an allowed principal and must be available in at least more than 50% of the Availability Zones in an AWS Region.

Set up a flow in Amazon AppFlow

We configure a new flow in Amazon AppFlow to run an ETL job on data from SAP to an S3 bucket. This flow allows for configuration of the SAP OData Connector as source, S3 bucket as destination, OData object selection, data mapping, data validation, and data filtering.

1. Configure the SAP OData Connector as a data source by providing the following information:
   1. Application host URL
   2. Application service path (catalog path)
   3. Port number
   4. Client number
   5. Logon language
   6. Connection type (private link or public)
   7. Authentication mode
   8. Connection name for the configuration
      
2. After you configure the source, choose the OData object and subobject for the sales orders.
   Generally, sales data from SAP is exported at a certain frequency, such as monthly or quarterly for the full size. For this post, choose the subobject option for the full-size export.
   
3. Choose the S3 bucket as the destination.
   The flow exports data to this bucket.
   
4. For Data format preference, select CSV format.
5. For Data transfer preference, select Aggregate all records.
6. For Filename preference, select Add a timestamp to the file name.
7. For Folder structure preference, select No timestamped folder.
   The record aggregation configuration exports the full-size sales data from SAP combined in a single file. The file name ends with a timestamp in the YYYY-MM-DDTHH:mm:ss format in a single folder (flow name) within the S3 bucket. Canvas imports data from this single file for model training and forecasting.
   
8. Configure data mapping and validations to map the source data fields to destination data fields, and enable data validation rules as required.
   
9. You also configure data filtering conditions to filter out specific records if your requirement demands.
   
10. Configure your flow trigger to decide whether the flow runs manually on-demand or automatically based on a schedule.
    When configured for a schedule, the frequency is based on how frequently the forecast needs to be generated (generally monthly, quarterly, or half-yearly).
    After the flow is configured, the business analysts can run it on demand or based on the schedule to perform an ETL job on the sales order data from SAP to an S3 bucket.
11. In addition to the Amazon AppFlow configuration, the data engineers also need to configure an AWS Identity and Access Management (IAM) role for Canvas so that it can access other AWS services. For instructions, refer to Give your users permissions to perform time series forecasting.

Business analyst: Use the historical sales data to train a forecasting model

Let’s switch gears and move to the business analyst side. As a business analyst, we’re looking for a visual, point-and-click service that makes it easy to build ML models and generate accurate predictions without writing a single line of code or having ML expertise. Canvas fits the requirement as no-code ML solution.

First, make sure that your IAM role is configured in such a way that Canvas can access other AWS services. For more information, refer to Give your users permissions to perform time series forecasting, or you can ask for help to your Cloud Engineering team.

When the data engineer is done setting up the Amazon AppFlow-based ETL configuration, the historical sales data is available for you in an S3 bucket.

You’re now ready to train a model with Canvas! This typically involves four steps: importing data into the service, configuring the model training by selecting the appropriate model type, training the model, and finally generating forecasts using the model.

Import data in Canvas

First, launch the Canvas app from the Amazon SageMaker console or from your single sign-on access. If you don’t know how to do that, contact your administrator so that they can guide you through the process of setting up Canvas. Make sure that you access the service in the same Region as the S3 bucket containing the historical dataset from SAP. You should see a screen like the following.

Then complete the following steps:

1. In Canvas, choose Datasets in the navigation pane.
2. Choose Import to start importing data from the S3 bucket.
   
3. On the import screen, choose the data file or object from the S3 bucket to import the training data.
   

You can import multiple datasets in Canvas. It also supports creating joins between the datasets by choosing Join data, which is particularly useful when the training data is spread across multiple files.

Configure and train the model

After you import the data, complete the following steps:

1. Choose Models in the navigation pane.
2. Choose New model to start configuration for training the forecast model.
   
3. For the new model, give it a suitable name, such as product_sales_forecast_model.
4. Select the sales dataset and choose Select dataset.
   
   After the dataset is selected, you can see data statistics and configure the model training on the Build tab.
   
5. Select totalsales as the target column for the prediction.
   You can see Time series forecasting is automatically selected as the model type.
6. Choose Configure.
   
7. In the Time series forecasting configuration section, choose productid for Item ID column.
8. Choose city for Group column.
9. Choose saledate for Time stamp column.
10. For Days, enter 120.
11. Choose Save.
    This configures the model to make forecasts for totalsales for 120 days using saledate based on historical data, which can be queried for productid and city.
    
12. When the model training configuration is complete, choose Standard Build to start the model training.

The Preview model option is not available for time series forecasting model type. You can review the estimated time for the model training on the Analyze tab.

Model training might take 1–4 hours to complete, depending on the data size. When the model is ready, you can use it to generate the forecast.

Generate a forecast

When the model training is complete, it shows prediction accuracy of the model on the Analyze tab. For instance, in this example, it shows prediction accuracy as 92.87%.

The forecast is generated on the Predict tab. You can generate forecasts for all the items or a selected single item. It also shows the date range for which the forecast can be generated.

As an example, choose the Single item option. Select P-2 for Item and Quito for Group to generate a prediction for product P-2 for city Quito for the date range 2017-08-15 00:00:00 through 2017-12-13 00:00:00.

The generated forecast shows the average forecast as well as the upper and lower bound of the forecast. The forecast bounds help configure an aggressive or balanced approach for the forecast handling.

You can also download the generated forecast as a CSV file or image. The generated forecast CSV file is generally to used to work offline with the forecast data.

The forecast is now generated for the time series data. When a new baseline of data becomes available for the forecast, you can change the dataset in Canvas to retrain the forecast model using the new baseline.

You can retrain the model multiple times as and when the training data changes.

Conclusion

In this post, you learned how the Amazon AppFlow SAP OData Connector exports sales order data from the SAP system into an S3 bucket and then how to use Canvas to build a model for forecasting.

You can use Canvas for any SAP time series data scenarios, such as expense or revenue prediction. The entire forecast generation process is configuration driven. Sales managers and representatives can generate sales forecasts repeatedly per month or per quarter with a refreshed set of data in a fast, straightforward, and intuitive way without writing a single line of code. This helps improve productivity and enables quick planning and decisions.

To get started, learn more about Canvas and Amazon AppFlow using the following resources:

• Amazon SageMaker Canvas Developer Guide
• Announcing Amazon SageMaker Canvas – a Visual, No Code Machine Learning Capability for Business Analysts
• Extract data from SAP ERP and BW with Amazon AppFlow
• SAP OData Connector configuration

About the Authors

Brajendra Singh is solution architect in Amazon Web Services working with enterprise customers. He has strong developer background and is a keen enthusiast for data and machine learning solutions.

Davide Gallitelli is a Specialist Solutions Architect for AI/ML in the EMEA region. He is based in Brussels and works closely with customers throughout Benelux. He has been a developer since he was very young, starting to code at the age of 7. He started learning AI/ML at university, and has fallen in love with it since then.

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Amazon Polly breathes life into text by converting it into lifelike speech. This empowers developers and businesses to create applications that can converse in real time, thereby offering an enhanced interactive experience. Text-to-speech (TTS) in Amazon Polly supports a variety of languages and locales, which enables you to perform TTS conversion according to your preferences. Multiple factors guide this choice, such as geographic location and language locales.

Amazon Polly uses advanced deep learning technologies to synthesize text to speech in real time in various output formats, such as MP3, ogg vorbis, JSON, or PCM, across standard and neural engines. The Speech Synthesis Markup Language (SSML) support for Amazon Polly further bolsters the service’s capability to customize speech with a plethora of options, including controlling speech rate and volume, adding pauses, emphasizing certain words or phrases, and more.

In today’s world, businesses continue to expand across multiple geographic locations, and they’re continuously looking for mechanisms to improve personalized end-user engagement. For instance, you may require accurate pronunciation of certain words in a specific style pertaining to different geographical locations. Your business may also need to pronounce certain words and phrases in certain ways depending on their intended meaning. You can achieve this with the help of SSML tags provided by Amazon Polly.

This post aims to assist you in customizing pronunciation when dealing with a truly global customer base.

Modify pronunciation using phonemes

A phoneme can be considered as the smallest unit of speech. The <phoneme> SSML tag in Amazon Polly helps customize pronunciation based on phonemes using the IPA (International Phonetic Alphabets) or X-SAMPA (Extended Speech Assessment Methods Phonetic Alphabet). X-SAMPA is a representation of IPA in ASCII encoding. Phoneme tags are available and fully supported in both the standard and neural TTS engine. For example, the word “lead” can be pronounced as the present tense verb, or it can refer to the chemical element lead. We will discuss this with an example further in this blog post.

International Phonetic Alphabet

The IPA is used to portray sounds across different languages. For a list of phonemes Amazon Polly supports, refer to Phoneme and Viseme Tables for Supported Languages.

By default, Amazon Polly determines the pronunciation of the word in a specific format. Let’s use the example of the word “lead,” which can have different pronunciations when referring to the chemical element or the verb. In this example, when we provide the word “lead” as input, it’s spoken in the present tense form (without the use of any customizing SSML tags). The default pronunciation for L E A D by Amazon Polly is the present tense form of “lead.”

<speak>
The default pronunciation by Amazon Polly for L E A D is <break time = "300ms"/> lead,
which is the present tense form.
</speak>

To return the pronunciation of the chemical element lead (which can also be the verb in past tense), we can use phonemes along with IPA or X-SAMPA. IPA is generally used to customize the pronunciation of a word in a given language using phonemes:

<speak>
This is the pronunciation using the
<say-as interpret-as="characters">IPA</say-as> attribute
in the <say-as interpret-as="characters">SSML</say-as> tag. 
The verb form for L E A D is <break time="150ms"/> lead.
The chemical element <break time="150ms"/><phoneme alphabet="ipa" ph="lɛd">lead</phoneme> 
<break time="300ms"/>also has an identical spelling.
</speak>

Modify pronunciation by specifying parts of speech

If we consider the same example of pronouncing “lead,” we can also differentiate between the chemical element and the verb by specifying the parts of speech using the <w> SSML tag.

The <w> tag allows us to customize pronunciation by specifying parts of speech. You can configure the pronunciation in terms of verb (present simple or past tense), noun, adjective, preposition, and determiner. See the following example:

<speak>
The word<p> <say-as interpret-as="characters">lead</say-as></p> 
may be interpreted as either the present simple form <w role="amazon:VB">lead</w>, 
or the chemical element <w role="amazon:SENSE_1">lead</w>.
</speak>

Additionally, you can use the <sub> tag to indicate the pronunciation of acronyms and abbreviations:

<speak>
Polly is an <sub alias="Amazon Web Services">AWS</sub> 
offering providing text-to-Speech service. 
</speak>

Extended Speech Assessment Methods Phonetic Alphabet

The X-SAMPA transcription scheme is an extrapolation to the various language-specific SAMPA phoneme sets available.

The following snippet shows how you can use X-SAMPA to pronounce different variations of the word “lead”:

<speak>
This is the pronunciation using the X-SAMPA attribute, 
in the verb form <break time="1s"/> lead.
The chemical element <break time="1s"/> 
<phoneme alphabet='x-sampa' ph='lEd'>lead</phoneme> <break time="0.5s"/>
also has an identical spelling.
</speak>

The stress mark in IPA is usually represented by ˈ. We often encounter scenarios in which an apostrophe is used instead, which might give a different output than expected. In X-SAMPA, the stress mark is the double quotation mark, therefore we should use a single quotation mark for the word and specify the phonemic alphabet. See the following example:

<speak>
You say, <phoneme alphabet="ipa" ph="pɪˈkɑːn">pecan</phoneme>. 
</speak>

In the example above, we can see the character ˈ used for stressing the word. Similarly, the stress mark in X-SAMPA is shown in double quotation below:

<speak>
You say, <phoneme alphabet='x-sampa' ph='pI"kA:n'>pecan</phoneme>.
</speak>

Modify pronunciations using other SSML tags

You can use the <say as> tag to modify pronunciation by enabling the spell-out or character feature. Furthermore, it enhances pronunciations in terms of digits, fractions, unit, date, time, address, telephone, cardinal, and ordinal, and can also censor the text enclosed within the tag. For more information, refer to Controlling How Special Types of Words Are Spoken. Let’s look at examples of these attributes.

Date

By default, Amazon Polly speaks out different text inputs. However, for handling specific attributes such as dates, you can use the date attribute to customize pronunciation in the required format, such as month-day-year or day-month-year.

Without the date attribute, Amazon Polly provides the following output when speaking out dates:

<speak>
The default pronunciation when using date is 01-11-1996
</speak>

However, if you want the dates spoken in a specific format, the date attribute in the <say-as> tags helps customize the pronunciation:

<speak>
We will see the examples of different date formats using the date SSML tag.
The following date is written in the day-month-year format.
<say-as interpret-as="date" format="dmy">01-11-1995</say-as><break time="500ms"/>
The following date is written in the month-day-year format.
<say-as interpret-as="date" format="mdy">09-24-1995</say-as>
</speak>

Cardinal

This attribute represents a number in its cardinal format. For example, 124456 is pronounced “one hundred twenty four thousand four hundred fifty six”:

<speak> 
The following number is pronounced in it's cardinal form.
<say-as interpret-as="cardinal">124456</say-as>
</speak>

Ordinal

This attribute represents a number in its ordinal format. Without the ordinal attribute, the number is pronounced in its numerical form:

<speak>
The following number is pronounced in it's ordinal form 
without the use of any SSML attribute in the say as tag - 1242 
</speak>

If we want to pronounce 1242 as “one thousand two hundred forty second,” we can use the ordinal attribute:

<speak>
The following number is pronounced in it's ordinal form.
<say-as interpret-as="ordinal">1242</say-as>
</speak>

Digits

The digits attribute is used to speak out the numbers. For example, “1234” is pronounced as “one two three four”:

<speak>
The following number is pronounced as individual digits.
<say-as interpret-as="digits">1242</say-as>
</speak>

Fraction

The fraction attribute is used to customize the pronunciations in the fractional form:

<speak> 
The following are examples of pronunciations when 
<prosody volume="loud"> fraction</prosody>
is used as an attribute in the say -as tag. 
<break time="500ms"/>Seven one by two is pronounced as
<say-as interpret-as="fraction">7 ½ </say-as>
whereas three by twenty is pronounced as <say-as interpret-as="fraction">3/20</say-as>
</speak>

Time

The time attribute is used to measure the time across minutes and seconds:

<speak>
Polly also supports customizing pronunciation in terms of minutes and seconds. 
For example, <say-as interpret-as="time">2'42"</say-as>
</speak>

Expletive

The expletive attribute censors the text enclosed within the tags:

<speak> 
The value that is going to be censored is
<say-as interpret-as="expletive">this is not good</say-as>
You should have heard the beep sound.
</speak>

Telephone

To pronounce telephone numbers, you can use the telephone attribute to speak out telephone numbers instead of pronouncing them as standalone digits or as a cardinal number:

<speak>
The telephone number is 
<say-as interpret-as="telephone">1800 3000 9009</say-as>
</speak>

Address

The address attribute is used to customize the pronunciation of an address aligning to a specific format:

<speak> 
The address is<break time="1s"/>
<say-as interpret-as="address">440 Terry Avenue North, Seattle
WA 98109 USA</say-as>
</speak>

Lexicons

We’ve looked at some of the SSML tags readily available in Amazon Polly. Other use cases might require a higher degree of control for customized pronunciations. Lexicons help achieve this requirement. You can use lexicons when certain words need to be pronounced in a certain form that is uncommon to that specific language.

Another use case for lexicons is with the use of numeronyms, which are abbreviations formed with the help of numbers. For example, Y2K is pronounced as the “year 2000.” You can use lexicons to customize these pronunciations.

Amazon Polly supports lexicon files in .pls and .xml formats. For more information, see Managing Lexicons.

Conclusion

Amazon Polly SSML tags can help you customize pronunciation in a variety of ways. We hope that this post gives you a head start into the world of speech synthesis and powers your applications to provide more lifelike human interactions.

About the Authors

Abilashkumar P C is a Cloud Support Engineer at AWS. He works with customers providing technical troubleshooting guidance, helping them achieve their workloads at scale. Outside of work, he loves driving, following cricket, and reading.

Abhishek Soni is a Partner Solutions Architect at AWS. He works with customers to provide technical guidance for the best outcome of workloads on AWS.

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Edge is a term that refers to a location, far from the cloud or a big data center, where you have a computer device (edge device) capable of running (edge) applications. Edge computing is the act of running workloads on these edge devices. Machine learning at the edge (ML@Edge) is a concept that brings the capability of running ML models locally to edge devices. These ML models can then be invoked by the edge application. ML@Edge is important for many scenarios where raw data is collected from sources far from the cloud. These scenarios may also have specific requirements or restrictions:

• Low-latency, real-time predictions
• Poor or non-existing connectivity to the cloud
• Legal restrictions that don’t allow sending data to external services
• Large datasets that need to be preprocessed locally before sending responses to the cloud

The following are some of many use cases that can benefit from ML models running close to the equipment that generates the data used for the predictions:

• Security and safety – A restricted area where heavy machines operate in an automated port is monitored by a camera. If a person enters this area by mistake, a safety mechanism is activated to stop the machines and protect the human.
• Predictive maintenance – Vibration and audio sensors collect data from a gearbox of a wind turbine. An anomaly detection model processes the sensor data and identifies if anomalies with the equipment. If an anomaly is detected, the edge device can start a contingency measurement in real time to avoid damaging the equipment, like engage the breaks or disconnect the generator from the grid.
• Defect detection in production lines – A camera captures images of products on a conveyor belt and process the frames with an image classification model. If a defect is detected, the product can be discarded automatically without manual intervention.

Although ML@Edge can address many use cases, there are complex architectural challenges that need to be solved in order to have a secure, robust, and reliable design. In this post, you learn some details about ML@Edge, related topics, and how to use AWS services to overcome these challenges and implement a complete solution for your ML at the edge workload.

ML@Edge overview

There is a common confusion when it comes to ML@Edge and Internet of Things (IoT), therefore it’s important to clarify how ML@Edge is different from IoT and how they both could come together to provide a powerful solution in certain cases.

An edge solution that uses ML@Edge has two main components: an edge application and an ML model (invoked by the application) running on the edge device. ML@Edge is about controlling the lifecycle of one or more ML models deployed to a fleet of edge devices. The ML model lifecycle can start on the cloud side (on Amazon SageMaker, for instance) but normally ends on a standalone deployment of the model on the edge device. Each scenario demands different ML model lifecycles that can be composed by many stages, such as data collection; data preparation; model building, compilation, and deployment to the edge device; model loading and running; and repeating the lifecycle.

The ML@Edge mechanism is not responsible for the application lifecycle. A different approach should be adopted for that purpose. Decoupling the ML model lifecycle and application lifecycle gives you the freedom and flexibility to keep evolving them at different paces. Imagine a mobile application that embeds an ML model as a resource like an image or XML file. In this case, each time you train a new model and want to deploy it to the mobile phones, you need to redeploy the whole application. This consumes time and money, and can introduce bugs to your application. By decoupling the ML model lifecycle, you publish the mobile app one time and deploy as many versions of the ML model as you need.

But how does IoT correlate to ML@Edge? IoT relates to physical objects embedded with technologies like sensors, processing ability, and software. These objects are connected to other devices and systems over the internet or other communication networks, in order to exchange data. The following figure illustrates this architecture. The concept was initially created when thinking of simple devices that just collect data from the edge, perform simple local processing, and send the result to a more powerful computing unity that runs analytics processes that help people and companies in their decision-making. The IoT solution is responsible for controlling the edge application lifecycle. For more information about IoT, refer to Internet of things.

If you already have an IoT application, you can add ML@Edge capabilities to make the product more efficient, as shown in the following figure. Keep in mind that ML@Edge doesn’t depend on IoT, but you can combine them to create a more powerful solution. When you do that, you improve the potential of your simple device to generate real-time insights for your business faster than just sending data to the cloud for later processing.

If you’re creating a new edge solution from scratch with ML@Edge capabilities, it’s important to design a flexible architecture that supports both the application and ML model lifecycles. We provide some reference architectures for edge applications with ML@Edge later in this post. But first, let’s dive deeper into edge computing and learn how to choose the correct edge device for your solution, based on the restrictions of the environment.

Edge computing

Depending on how far the device is from the cloud or a big data center (base), three main characteristics of the edge devices need to be considered to maximize performance and longevity of the system: computing and storage capacity, connectivity, and power consumption. The following diagram shows three groups of edge devices that combine different specifications of these characteristics, depending on how far from they are from the base.

The groups are as follows:

• MECs (Multi-access Edge Computing) – MECs or small data centers, characterized by low or ultra-low latency and high bandwidth, are common environments where ML@Edge can bring benefits without big restrictions when compared to cloud workloads. 5G antennas and servers at factories, warehouses, laboratories, and so on with minimal energy constraints and with good internet connectivity offer different ways to run ML models on GPUs and CPUs, virtual machines, containers, and bare-metal servers.
• Near edge – This is when mobility or data aggregation are requirements and the devices have some constraints regarding power consumption and processing power, but still have some reliable connectivity, although with higher latency, with limited throughput and more expensive than “close to the edge.” Mobile applications, specific boards to accelerate ML models, or simple devices with capacity to run ML models, covered by wireless networks, are included in this group.
• Far edge – In this extreme scenario, edge devices have severe power consumption or connectivity constraints. Consequently, processing power is also restricted in many far edge scenarios. Agriculture, mining, surveillance and security, and maritime transportation are some areas where far edge devices play an important role. Simple boards, normally without GPUs or other AI accelerators, are common. They are designed to load and run simple ML models, save the predictions in a local database, and sleep until the next prediction cycle. The devices that need to process real-time data can have big local storages to avoid losing data.

Challenges

It’s common to have ML@Edge scenarios where you have hundreds or thousands (maybe even millions) of devices running the same models and edge applications. When you scale your system, it’s important to have a robust solution that can manage the number of devices that you need to support. This is a complex task and for these scenarios, you need to ask many questions:

• How do I operate ML models on a fleet of devices at the edge?
• How do I build, optimize, and deploy ML models to multiple edge devices?
• How do I secure my model while deploying and running it at the edge?
• How do I monitor my model’s performance and retrain it, if needed?
• How do I eliminate the need of installing a big framework like TensorFlow or PyTorch on my restricted device?
• How do I expose one or multiple models with my edge application as a simple API?
• How do I create a new dataset with the payloads and predictions captured by the edge devices?
• How do I do all these tasks automatically (MLOps plus ML@Edge)?

In the next section, we provide answers to all these questions through example use cases and reference architectures. We also discuss which AWS services you can combine to build complete solutions for each of the explored scenarios. However, if you want to start with a very simple flow that describes how to use some of the services provided by AWS to create your ML@Edge solution, this is an example:

With SageMaker, you can easily prepare a dataset and build the ML models that are deployed to the edge devices. With Amazon SageMaker Neo, you can compile and optimize the model you trained to the specific edge device you chose. After compiling the model, you only need a light runtime to run it (provided by the service). Amazon SageMaker Edge Manager is responsible for managing the lifecycle of all ML models deployed to your fleet of edge devices. Edge Manager can manage fleets of up to millions of devices. An agent, installed to each one of the edge devices, exposes the deployed ML models as an API to the application. The agent is also responsible for collecting metrics, payloads, and predictions that you can use for monitoring or building a new dataset to retrain the model if needed. Finally, with Amazon SageMaker Pipelines, you can create an automated pipeline with all the steps required to build, optimize, and deploy ML models to your fleet of devices. This automated pipeline can then be triggered by simple events you define, without human intervention.

Use case 1

Let’s say an airplane manufacturer wants to detect and track parts and tools in the production hangar. To improve productivity, all the required parts and correct tools need to be available for the engineers at each stage of production. We want to be able to answer questions like: Where is part A? or Where is tool B? We have multiple IP cameras already installed and connected to a local network. The cameras cover the entire hangar and can stream real-time HD video through the network.

AWS Panorama fits in nicely in this case. AWS Panorama provides an ML appliance and managed service that enables you to add computer vision (CV) to your existing fleet of IP cameras and automate. AWS Panorama gives you the ability to add CV to your existing Internet Protocol (IP) cameras and automate tasks that traditionally require human inspection and monitoring.

In the following reference architecture, we show the major components of the application running on an AWS Panorama Appliance. The Panorama Application SDK makes it easy to capture video from camera streams, perform inference with a pipeline of multiple ML models, and process the results using Python code running inside a container. You can run models from any popular ML library such as TensorFlow, PyTorch, or TensorRT. The results from the model can be integrated with business systems on your local area network, allowing you to respond to events in real time.

The solution consists of the following steps:

1. Connect and configure an AWS Panorama device to the same local network.
2. Train an ML model (object detection) to identify parts and tools in each frame.
3. Build an AWS Panorama Application that gets the predictions from the ML model, applies a tracking mechanism to each object, and sends the results to a real-time database.
4. The operators can send queries to the database to locate the parts and tools.

Use case 2

For our next use case, imagine we’re creating a dashcam for vehicles capable of supporting the driver in many situations, such as avoiding pedestrians, based on a CV25 board from Ambaralla. Hosting ML models on a device with limited system resources can be difficult. In this case, let’s assume we already have a well-established over-the-air (OTA) delivery mechanism in place to deploy the application components needed on to the edge device. However, we would still benefit from ability to do OTA deployment of the model itself, thereby isolating the application lifecycle and model lifecycle.

Amazon SageMaker Edge Manager and Amazon SageMaker Neo fit well for this use case.

Edge Manager makes it easy for ML edge developers to use the same familiar tools in the cloud or on edge devices. It reduces the time and effort required to get models to production, while allowing you to continuously monitor and improve model quality across your device fleet. SageMaker Edge includes an OTA deployment mechanism that helps you deploy models on the fleet independent of the application or device firmware. The Edge Manager agent allows you to run multiple models on the same device. The agent collects prediction data based on the logic that you control, such as intervals, and uploads it to the cloud so that you can periodically retrain your models over time. SageMaker Edge cryptographically signs your models so you can verify that it wasn’t tampered with as it moves from the cloud to edge device.

Neo is a compiler as a service and an especially good fit in this use case. Neo automatically optimizes ML models for inference on cloud instances and edge devices to run faster with no loss in accuracy. You start with an ML model built with one of supported frameworks and trained in SageMaker or anywhere else. Then you choose your target hardware platform, (refer to the list of supported devices). With a single click, Neo optimizes the trained model and compiles it into a package that can be run using the lightweight SageMaker Edge runtime. The compiler uses an ML model to apply the performance optimizations that extract the best available performance for your model on the cloud instance or edge device. You then deploy the model as a SageMaker endpoint or on supported edge devices and start making predictions.

The following diagram illustrates this architecture.

The solution workflow consists of the following steps:

1. The developer builds, trains, validates, and creates the final model artefact that needs to be deployed to the dashcam.
2. Invoke Neo to compile the trained model.
3. The SageMaker Edge agent is installed and configured on the Edge device, in this case the dashcam.
4. Create a deployment package with a signed model and the runtime used by the SageMaker Edge agent to load and invoke the optimized model.
5. Deploy the package using the existing OTA deployment mechanism.
6. The edge application interacts with the SageMaker Edge agent to do inference.
7. The agent can be configured (if required) to send real-time sample input data from the application for model monitoring and refinement purposes.

Use case 3

Suppose your customer is developing an application that detects anomalies in the mechanisms of a wind turbine (like the gearbox, generator, or rotor). The goal is to minimize the damage on the equipment by running local protection procedures on the fly. These turbines are very expensive and located in places that aren’t easily accessible. Each turbine can be outfitted with an NVIDIA Jetson device to monitor sensor data from the turbine. We then need a solution to capture the data and use an ML algorithm to detect anomalies. We also need an OTA mechanism to keep the software and ML models on the device up to date.

AWS IoT Greengrass V2 along with Edge Manager fit well in this use case. AWS IoT Greengrass is an open-source IoT edge runtime and cloud service that helps you build, deploy, and manage IoT applications on your devices. You can use AWS IoT Greengrass to build edge applications using pre-built software modules, called components, that can connect your edge devices to AWS services or third-party services. This ability of AWS IoT Greengrass makes it easy to deploy assets to devices, including a SageMaker Edge agent. AWS IoT Greengrass is responsible for managing the application lifecycle, while Edge Manager decouples the ML model lifecycle. This gives you the flexibility to keep evolving the whole solution by deploying new versions of the edge application and ML models independently. The following diagram illustrates this architecture.

The solution consists of the following steps:

1. The developer builds, trains, validates, and creates the final model artefact that needs to be deployed to the wind turbine.
2. Invoke Neo to compile the trained model.
3. Create a model component using Edge Manager with AWS IoT Greengrass V2 integration.
4. Set up AWS IoT Greengrass V2.
5. Create an inference component using AWS IoT Greengrass V2.
6. The edge application interacts with the SageMaker Edge agent to do inference.
7. The agent can be configured (if required) to send real-time sample input data from the application for model monitoring and refinement purposes.

Use case 4

For our final use case, let’s look at a vessel transporting containers, where each container has a couple of sensors and streams a signal to the compute and storage infrastructure deployed locally. The challenge is that we want to know the content of each container, and the condition of the goods based on temperature, humidity, and gases inside each container. We also want to track all the goods in each one of the containers. There is no internet connectivity throughout the voyage, and the voyage can take months. The ML models running on this infrastructure should preprocess the data and generate information to answer all our questions. The data generated needs to be stored locally for months. The edge application stores all the inferences in a local database and then synchronizes the results with the cloud when the vessel approaches the port.

AWS Snowcone and AWS Snowball from the AWS Snow Family could fit very well in this use case.

AWS Snowcone is a small, rugged, and secure edge computing and data migration device. Snowcone is designed to the OSHA standard for a one-person liftable device. Snowcone enables you to run edge workloads using Amazon Elastic Compute Cloud (Amazon EC2) computing, and local storage in harsh, disconnected field environments such as oil rigs, search and rescue vehicles, military sites, or factory floors, as well as remote offices, hospitals, and movie theaters.

Snowball adds more computing when compared to Snowcone and therefore may be a great fit for more demanding applications. The Compute Optimized feature provides an optional NVIDIA Tesla V100 GPU along with EC2 instances to accelerate an application’s performance in disconnected environments. With the GPU option, you can run applications such as advanced ML and full motion video analysis in environments with little or no connectivity.

On top of the EC2 instance, you have the freedom to build and deploy any type of edge solution. For instance: you can use Amazon ECS or other container manager to deploy the edge application, Edge Manager Agent and the ML model as individual containers. This architecture would be similar to Use Case 2 (except that it will work offline most of the time), with the addition of a container manager tool.

The following diagram illustrates this solution architecture.

To implement this solution, simply order your Snow device from the AWS Management Console and launch your resources.

Conclusion

In this post, we discussed the different aspects of edge that you may choose to work with based on your use case. We also discussed some of the key concepts around ML@Edge and how decoupling the application lifecycle and the ML model lifecycle gives you the freedom to evolve them without any dependency on each other. We emphasized how choosing the right edge device for your workload and asking the right questions during the solution process can help you work backward and narrow down the right AWS services. We also presented different use cases along with reference architectures to inspire you to create your own solutions that will work for your workload.

About the Authors

Dinesh Kumar Subramani is a Senior Solutions Architect with the UKIR SMB team, based in Edinburgh, Scotland. He specializes in artificial intelligence and machine learning. Dinesh enjoys working with customers across industries to help them solve their problems with AWS services. Outside of work, he loves spending time with his family, playing chess and enjoying music across genres.

Samir Araújo is an AI/ML Solutions Architect at AWS. He helps customers creating AI/ML solutions which solve their business challenges using AWS. He has been working on several AI/ML projects related to computer vision, natural language processing, forecasting, ML at the edge, and more. He likes playing with hardware and automation projects in his free time, and he has a particular interest for robotics.

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