Monthly Archives: October 2023

Generative artificial intelligence is transforming how enterprises do business. Organizations are using AI to improve data-driven decisions, enhance omnichannel experiences, and drive next-generation product development. Enterprises are using generative AI specifically to power their marketing efforts through emails, push notifications, and other outbound communication channels. Gartner predicts that “by 2025, 30% of outbound marketing messages from large organizations will be synthetically generated.” However, generative AI alone isn’t enough to deliver engaging customer communication. Research shows that the most impactful communication is personalized—showing the right message to the right user at the right time. According to McKinsey, “71% of consumers expect companies to deliver personalized interactions.” Customers can use Amazon Personalize and generative AI to curate concise, personalized content for marketing campaigns, increase ad engagement, and enhance conversational chatbots.

Developers can use Amazon Personalize to build applications powered by the same type of machine learning (ML) technology used by Amazon.com for real-time personalized recommendations. With Amazon Personalize, developers can improve user engagement through personalized product and content recommendations with no ML expertise required. Using recipes (algorithms prepared to support specific uses cases) provided by Amazon Personalize, customers can deliver a wide array of personalization, including specific product or content recommendations, personalized ranking, and user segmentation. Additionally, as a fully managed artificial intelligence service, Amazon Personalize accelerates customers’ digital transformations with ML, making it easier to integrate personalized recommendations into existing websites, applications, email marketing systems, and so on.

In this post, we illustrate how you can elevate your marketing campaigns using Amazon Personalize and generative AI with Amazon Bedrock. Together, Amazon Personalize and generative AI help you tailor your marketing to individual consumer preferences.

How exactly do Amazon Personalize and Amazon Bedrock work together to achieve this? Imagine as a marketer that you want to send tailored emails to users recommending movies they would enjoy based on their interactions across your platform. Or perhaps you want to send targeted emails to a segment of users promoting a new shoe they might be interested in. The following use cases use generative AI to enhance two common marketing emails.

Use Case 1: Use generative AI to deliver targeted one-to-one personalized emails

With Amazon Personalize and Amazon Bedrock, you can generate personalized recommendations and create outbound messages with a personal touch tailored to each of your users.

The following diagram illustrates the architecture and workflow for delivering targeted personalized emails powered by generative AI.

First, import your dataset of users’ interactions into Amazon Personalize for training. Amazon Personalize automatically trains a model using the Top Picks for You recipe. As an output, Amazon Personalize provides recommendations that align with the users’ preferences.

You can use the following code to identify recommended items for users:

get_recommendations_response = personalize_runtime.get_recommendations(
                            recommenderArn = workshop_recommender_top_picks_arn,
                            userId = str(user_id),
                            numResults = number_of_movies_to_recommend)

For more information, see the Amazon Personalize API reference.

The provided output of recommendations is ingested by Amazon Bedrock using a prompt, which includes your user preferences, demographics, and Amazon Personalize recommended items.

For example, a marketer who wants to create a personalized email that is charming and fun for a user might use the following prompt:

Create a personalized email which is charming and fun so that the user is engaged. The user has recently watched family-friendly films. These are the recommended items – The Little Mermaid, Encanto, Spider-Man: Into the Spider-Verse.

By invoking one of the foundation models (FM) provided in Amazon Bedrock, such as Claude 2, with the prompt and sample code that follows, you can create a personalized email for a user:

personalized_email_response = bedrock_client.invoke_model(
                            body = prompt,
                            modelId = identifier_of_the_model)

For more information, see the Amazon Bedrock API reference.

Amazon Bedrock returns a personalized email for the user:

Subject: Fall in love with this recommended selection for movie night!

Dear <user name>,

Desiring the cozy feel of fall? No problem! Check our top three recommendations for movies that will have you cozy on the couch with your loved ones:

1. The Little Mermaid: This classic Disney movie is all about a mermaid princess named Ariel, who dreams of the human world. Because of her fascination, she makes a deal with the sea witch Ursula and learns a major lesson.

2. Encanto: This Disney movie is about the Madrigals, a Colombian family who lives in a magical house. Each member of the family has a unique gift, except for young Maribel who must help save her family.

3. Spider-Man: Into the Spider-Verse: This animated superhero movie is a must-see action movie. Spider-man, a Brooklyn teen named Miles Morales, teams up with other spider-powered people to save the multiverse.

With lovable characters, catchy tunes, and moving stories, you really can’t go wrong with any of these three. Grab the popcorn because you’re in for a treat!

Use case 2: Use generative AI to elevate one-to-many marketing campaigns

When it comes to one-to-many email marketing, generic content can result in low engagement (that is, low open rates and unsubscribes). One way companies circumvent this outcome is to manually craft variations of outbound messages with compelling subjects. This can lead to inefficient use of time. By integrating Amazon Personalize and Amazon Bedrock into your workflow, you can quickly identify the interested segment of users and create variations of email content with greater relevance and engagement.

The following diagram illustrates the architecture and workflow for elevating marketing campaigns powered by generative AI.

To compose one-to-many emails, first import your dataset of users’ interactions into Amazon Personalize for training. Amazon Personalize trains the model using the user segmentation recipe. With the user segmentation recipe, Amazon Personalize automatically identifies the individual users that demonstrate a propensity for the chosen items as the target audience.

To identify the target audience and retrieve metadata for an item you can use the following sample code:

create_batch_segment_response = personalize.create_batch_segment_job(
        jobName = job_name,
        solutionVersionArn = solution_version_arn,
        numResults = number_of_users_to_recommend
        jobInput =  {
            "s3DataSource": {
                "path": batch_input_path
            }
        },
        jobOutput = {
            "s3DataDestination": {
            "path": batch_output_path
            }
        }
)

For more information, see the Amazon Personalize API reference.

Amazon Personalize delivers a list of recommended users to target for each item to batch_output_path. You can then invoke the user segment into Amazon Bedrock using one of the FMs along with your prompt.

For this use case, you might want to market a newly released sneaker through email. An example prompt might include the following:

For the user segment “sneaker heads”, create a catchy email that promotes the latest sneaker “Ultra Fame II”. Provide users with discount code FAME10 to save 10%.

Similar to the first use case, you’ll use the following code in Amazon Bedrock:

personalized_email_response = bedrock_client.invoke_model(
                                body = prompt,
                                modelId = identifier_of_the_model)

For more information, see the Amazon Bedrock API reference.

Amazon Bedrock returns a personalized email based on the items chosen for each user as shown:

Subject: <<name>>, your ticket to the Hall of Fame awaits

Hey <<name>>,

The wait is over. Check out the new Ultra Fame II! It’s the most innovative and comfortable Ultra Fame shoe yet. Its new design will have you turning heads with every step. Plus, you’ll get a mix of comfort, support, and style that’s just enough to get you into the Hall of Fame.

Don’t wait until it’s too late. Use the code FAME10 to save 10% on your next pair.

To test and determine the email that leads to the highest engagement, you can use Amazon Bedrock to generate a variation of catchy subject lines and content in a fraction of the time it would take to manually produce test content.

Conclusion

By integrating Amazon Personalize and Amazon Bedrock, you are enabled to deliver personalized promotional content to the right audience.

Generative AI powered by FMs is changing how businesses build hyper-personalized experiences for consumers. AWS AI services, such as Amazon Personalize and Amazon Bedrock, can help recommend and deliver products, content, and compelling marketing messages personalized to your users. For more information on working with generative AI on AWS, see to Announcing New Tools for Building with Generative AI on AWS.

About the Authors

Ba’Carri Johnson is a Sr. Technical Product Manager working with AWS AI/ML on the Amazon Personalize team. With a background in computer science and strategy, she is passionate about product innovation. In her spare time, she enjoys traveling and exploring the great outdoors.

Ragini Prasad is a Software Development Manager with the Amazon Personalize team focused on building AI-powered recommender systems at scale. In her spare time, she enjoys art and travel.

Jingwen Hu is a Sr. Technical Product Manager working with AWS AI/ML on the Amazon Personalize team. In her spare time, she enjoys traveling and exploring local food.

Anna Grüebler is a Specialist Solutions Architect at AWS focusing on artificial intelligence. She has more than 10 years of experience helping customers develop and deploy machine learning applications. Her passion is taking new technologies and putting them in the hands of everyone and solving difficult problems by taking advantage of using AI in the cloud.

Tim Wu Kunpeng is a Sr. AI Specialist Solutions Architect with extensive experience in end-to-end personalization solutions. He is a recognized industry expert in e-commerce and media and entertainment, with expertise in generative AI, data engineering, deep learning, recommendation systems, responsible AI, and public speaking.

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Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra helps you easily aggregate content from a variety of content repositories into a centralized index that lets you quickly search all your enterprise data and find the most accurate answer. Drupal is a content management software. It’s used to make many of the websites and applications we use every day. Drupal has a great feature set, like straightforward content authoring, reliable performance, and security. Many organizations use Drupal to store their content. One of the key requirements for many customers using Drupal is the ability to easily and securely find accurate information across all the documents in the data source.

With the Amazon Kendra Drupal connector, you can index Drupal content, filter the types of custom content you want to index, and easily search through Drupal content using Amazon Kendra intelligent search.

This post shows you how to use the Amazon Kendra Drupal connector to configure the connector as a data source for your Amazon Kendra index and search your Drupal documents. Based on the configuration of the Drupal connector, you can synchronize the connector to crawl and index different types of Drupal content such as blogs and wikis. The connector also ingests the access control list (ACL) information for each file. The ACL information is used for user context filtering, where search results for a query are filtered by what a user has authorized access to.

Prerequisites

To try out the Amazon Kendra connector for Drupal using this post as a reference, you need the following:

• An AWS account with privileges to create AWS Identity and Access Management (IAM) roles and policies. For more information, see Overview of access management: Permissions and policies and IAM roles for Drupal data sources.
• Basic knowledge of AWS and working knowledge of Drupal administration.
• Drupal set up with a user with the Administrator role. We will store the administrator user name and password in AWS Secrets Manager.

Configure the data source using the Amazon Kendra connector for Drupal

To add a data source to your Amazon Kendra index using the Drupal connector, you can use an existing index or create a new index. Then complete the following steps. For more information on this topic, refer to the Amazon Kendra Developer Guide.

1. On the Amazon Kendra console, open your index and choose Data sources in the navigation pane.
2. Choose Add data source.
3. Under Drupal, choose Add connector.
   
4. In the Specify data source details section, enter a name and description and choose Next.
   
5. On the Define access and security section, for Drupal Host URL, enter the Drupal site URL.
6. To configure the SSL certificates, you can create a self-signed certificate for this setup using the openssl x509 -in mydrupalsite.pem -out drupal.crt command and store the certificate in an Amazon Simple Storage Service (Amazon S3) bucket. For more details on generating a private key and the certificate, refer to Generating Certificates.
7. Choose Browse S3 and choose the S3 bucket with the SSL certificate.
   
8. Under Authentication, you have two options:
   • Use Secrets Manager to create new Drupal authentication credentials. You need a Drupal admin user name and password (additionally, a client ID and client secret for OAuth 2.0 authentication).
   • Use an existing Secrets Manager secret that has the Drupal authentication credentials you want the connector to access (additionally, a client ID and client secret for OAuth 2.0 authentication).
9. Choose Save and add secret.
   
10. For IAM role, choose Create a new role or choose an existing IAM role configured with appropriate IAM policies to access the Secrets Manager secret, Amazon Kendra index, and data source.

Refer to IAM roles for data sources for the required permissions for the IAM role.

11. Choose Next.
    
12. In the Configure sync settings section, select Articles, Basic pages, Basic blocks, Custom content types, and Custom Blocks along with options to crawl comments and attachments as needed.
13. Optionally, enter the include/exclude patterns for the entity titles.
14. Provide information about your sync scope (full or delta only) and specify the run schedule.
15. Choose Next.
    
    
16. In the Set field mappings section, add custom Drupal fields you want to sync and their respective Amazon Kendra field mappings. The required fields are pre-mapped by Amazon Kendra.
    
17. Choose Next.
18. Review the configuration settings and save the data source.
19. Choose Sync now on the created data source to start data synchronization with the Amazon Kendra Index.
    

The time required to crawl and sync the contents into Amazon Kendra varies based on the volume of content and the throughput.

You can now search the indexed Drupal content using the search console or a search application. Optionally, you can search with ACL with the following additional steps.

20. Go to the index page that you created and on the User access control tab, choose Edit settings.
    
21. Under Access control settings, select Yes, keep the default values for Username and Groups, choose JSON for Token type, and keep the user-group expansion as None.
22. On the next page, retain the default values (or change them based on your capacity requirements) and choose Update.
    

Perform intelligent search with Amazon Kendra

Before you try searching on the Amazon Kendra console or using the API, make sure that the data source sync is complete. To check, view the data sources and verify if the last sync was successful.

1. To start your search, on the Amazon Kendra console, choose Search indexed content in the navigation pane.

You’re redirected to the Amazon Kendra search console. Now you can search information from the Drupal documents you indexed using Amazon Kendra.

2. For this post, we search for a document stored in the Drupal data source.
   
3. Expand Test query with an access token and choose Apply token.
4. For Username, enter the email address associated with your Drupal account.
5. Choose Apply.
   

Now the user can only see the content they have access based on the user name or groups specified. In our example, the Drupal user with the test@amazon.com email doesn’t have access to any documents on Drupal, so none are displayed.

Limitations

Note the following limitations when using this solution:

• The content types (such as article, or basic page) that aren’t associated with any view cannot be crawled.
• If an administrator doesn’t have access to a block, then you can’t crawl the data from the block.
• The document body for article, basic page, basic block, user-defined content type, and user-defined block type is displayed in HTML format. If the HTML content is not well-formed, then the HTML related tags will appear in the document body and therefore can be seen on the Amazon Kendra search results. This is the same with comments of article, basic page, basic block, user-defined content type, user-defined block type.
• The content type or block type without description or body will not be injected into the Amazon Kendra index because there is a validation on the Amazon Kendra SDK side. However, Drupal allows you to create the content type without description or body. Only the comments and attachments of the respective content types or block types (if they exist) will be injected into the Amazon Kendra index.

Clean up

To avoid incurring future costs, clean up the resources you created as part of this solution. If you created a new Amazon Kendra index while testing this solution, delete it. If you only added a new data source using the Amazon Kendra connector for Drupal, delete that data source. Delete any IAM users created.

Conclusion

With the Amazon Kendra Drupal connector, your organization can search contents stored in a Drupal site securely using intelligent search powered by Amazon Kendra. In this post, we introduced you to the integration, but there are many additional features that we didn’t cover, such as the following:

• You can map additional fields to Amazon Kendra index attributes and enable them for faceting, search, and display in the search results
• You can integrate the Drupal data source with the Custom Document Enrichment (CDE) capability in Amazon Kendra to perform additional attribute mapping logic and even custom content transformation during ingestion

To learn more about the possibilities with Drupal, refer to the Amazon Kendra Developer Guide.

For more information on other Amazon Kendra built-in connectors for popular data sources, refer to the Amazon Kendra Connectors page.

About the authors

Channa Basavaraja is a Senior Solutions Architect at AWS with over 2 decades of experience building distributed business solutions. His areas of depth span Machine Learning, app/mobile dev, event-driven architecture, and IoT/edge computing.

Yuanhua Wang is a software engineer at AWS with more than 15 years of experience in the technology industry. His interests are software architecture and build tools on cloud computing.

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This is a guest post by Jose Benitez, Founder and Director of AI and Mattias Ponchon, Head of Infrastructure at Intuitivo.

Intuitivo, a pioneer in retail innovation, is revolutionizing shopping with its cloud-based AI and machine learning (AI/ML) transactional processing system. This groundbreaking technology enables us to operate millions of autonomous points of purchase (A-POPs) concurrently, transforming the way customers shop. Our solution outpaces traditional vending machines and alternatives, offering an economical edge with its ten times cheaper cost, easy setup, and maintenance-free operation. Our innovative new A-POPs (or vending machines) deliver enhanced customer experiences at ten times lower cost because of the performance and cost advantages AWS Inferentia delivers. Inferentia has enabled us to run our You Only Look Once (YOLO) computer vision models five times faster than our previous solution and supports seamless, real-time shopping experiences for our customers. Additionally, Inferentia has also helped us reduce costs by 95 percent compared to our previous solution. In this post, we cover our use case, challenges, and a brief overview of our solution using Inferentia.

The changing retail landscape and need for A-POP

The retail landscape is evolving rapidly, and consumers expect the same easy-to-use and frictionless experiences they are used to when shopping digitally. To effectively bridge the gap between the digital and physical world, and to meet the changing needs and expectations of customers, a transformative approach is required. At Intuitivo, we believe that the future of retail lies in creating highly personalized, AI-powered, and computer vision-driven autonomous points of purchase (A-POP). This technological innovation brings products within arm’s reach of customers. Not only does it put customers’ favorite items at their fingertips, but it also offers them a seamless shopping experience, devoid of long lines or complex transaction processing systems. We’re excited to lead this exciting new era in retail.

With our cutting-edge technology, retailers can quickly and efficiently deploy thousands of A-POPs. Scaling has always been a daunting challenge for retailers, mainly due to the logistic and maintenance complexities associated with expanding traditional vending machines or other solutions. However, our camera-based solution, which eliminates the need for weight sensors, RFID, or other high-cost sensors, requires no maintenance and is significantly cheaper. This enables retailers to efficiently establish thousands of A-POPs, providing customers with an unmatched shopping experience while offering retailers a cost-effective and scalable solution.

Using cloud inference for real-time product identification

While designing a camera-based product recognition and payment system, we ran into a decision of whether this should be done on the edge or the cloud. After considering several architectures, we designed a system that uploads videos of the transactions to the cloud for processing.

Our end users start a transaction by scanning the A-POP’s QR code, which triggers the A-POP to unlock and then customers grab what they want and go. Preprocessed videos of these transactions are uploaded to the cloud. Our AI-powered transaction pipeline automatically processes these videos and charges the customer’s account accordingly.

The following diagram shows the architecture of our solution.

Unlocking high-performance and cost-effective inference using AWS Inferentia

As retailers look to scale operations, cost of A-POPs becomes a consideration. At the same time, providing a seamless real-time shopping experience for end-users is paramount. Our AI/ML research team focuses on identifying the best computer vision (CV) models for our system. We were now presented with the challenge of how to simultaneously optimize the AI/ML operations for performance and cost.

We deploy our models on Amazon EC2 Inf1 instances powered by Inferentia, Amazon’s first ML silicon designed to accelerate deep learning inference workloads. Inferentia has been shown to reduce inference costs significantly. We used the AWS Neuron SDK—a set of software tools used with Inferentia—to compile and optimize our models for deployment on EC2 Inf1 instances.

The code snippet that follows shows how to compile a YOLO model with Neuron. The code works seamlessly with PyTorch and functions such as torch.jit.trace()and neuron.trace()record the model’s operations on an example input during the forward pass to build a static IR graph.

from ultralytics import YOLO
import torch_neuronx
import torch

batch_size = 1
imgsz = (640, 640)
im = torch.zeros(batch_size, 3, *imgsz).to('cpu')  # mock input

# Compiler options
half = True  # fp16
fp8 = False
dynamic = False  # dynamic batch

f = 'yolov8n.neuronx'  # output model name
neuronx_cc_args = ['--auto-cast', 'none']

if half:
    neuronx_cc_args = ['--auto-cast', 'all', '--auto-cast-type', 'fp16']
elif fp8:
    neuronx_cc_args = ['--auto-cast', 'all', '--auto-cast-type', 'fp8_e4m3']

model = torch.load('yolov8n.pt')['model']
model.eval()
model.float()
model = model.fuse()
neuronx_model = torch_neuronx.trace(
    model,
    example_inputs=im,
    compiler_args=neuronx_cc_args,
)

if dynamic:
    neuronx_model = torch_neuronx.dynamic_batch(neuronx_model)

neuronx_model.save(f)

We migrated our compute-heavy models to Inf1. By using AWS Inferentia, we achieved the throughput and performance to match our business needs. Adopting Inferentia-based Inf1 instances in the MLOps lifecycle was a key to achieving remarkable results:

1. Performance improvement: Our large computer vision models now run five times faster, achieving over 120 frames per second (FPS), allowing for seamless, real-time shopping experiences for our customers. Furthermore, the ability to process at this frame rate not only enhances transaction speed, but also enables us to feed more information into our models. This increase in data input significantly improves the accuracy of product detection within our models, further boosting the overall efficacy of our shopping systems.
2. Cost savings: We slashed inference costs. This significantly enhanced the architecture design supporting our A-POPs.

Data parallel inference was easy with AWS Neuron SDK

To improve performance of our inference workloads and extract maximum performance from Inferentia, we wanted to use all available NeuronCores in the Inferentia accelerator. Achieving this performance was easy with the built-in tools and APIs from the Neuron SDK. We used the torch.neuron.DataParallel() API. We’re currently using inf1.2xlarge which has one Inferentia accelerator with four Neuron accelerators. So we’re using torch.neuron.DataParallel() to fully use the Inferentia hardware and use all available NeuronCores. This Python function implements data parallelism at the module level on models created by the PyTorch Neuron API. Data parallelism is a form of parallelization across multiple devices or cores (NeuronCores for Inferentia), referred to as nodes. Each node contains the same model and parameters, but data is distributed across the different nodes. By distributing the data across multiple nodes, data parallelism reduces the total processing time of large batch size inputs compared to sequential processing. Data parallelism works best for models in latency-sensitive applications that have large batch size requirements.

Looking ahead: Accelerating retail transformation with foundation models and scalable deployment

As we venture into the future, the impact of foundation models on the retail industry cannot be overstated. Foundation models can make a significant difference in product labeling. The ability to quickly and accurately identify and categorize different products is crucial in a fast-paced retail environment. With modern transformer-based models, we can deploy a greater diversity of models to serve more of our AI/ML needs with higher accuracy, improving the experience for users and without having to waste time and money training models from scratch. By harnessing the power of foundation models, we can accelerate the process of labeling, enabling retailers to scale their A-POP solutions more rapidly and efficiently.

We have begun implementing Segment Anything Model (SAM), a vision transformer foundation model that can segment any object in any image (we will discuss this further in another blog post). SAM allows us to accelerate our labeling process with unparalleled speed. SAM is very efficient, able to process approximately 62 times more images than a human can manually create bounding boxes for in the same timeframe. SAM’s output is used to train a model that detects segmentation masks in transactions, opening up a window of opportunity for processing millions of images exponentially faster. This significantly reduces training time and cost for product planogram models.

Our product and AI/ML research teams are excited to be at the forefront of this transformation. The ongoing partnership with AWS and our use of Inferentia in our infrastructure will ensure that we can deploy these foundation models cost effectively. As early adopters, we’re working with the new AWS Inferentia 2-based instances. Inf2 instances are built for today’s generative AI and large language model (LLM) inference acceleration, delivering higher performance and lower costs. Inf2 will enable us to empower retailers to harness the benefits of AI-driven technologies without breaking the bank, ultimately making the retail landscape more innovative, efficient, and customer-centric.

As we continue to migrate more models to Inferentia and Inferentia2, including transformers-based foundational models, we are confident that our alliance with AWS will enable us to grow and innovate alongside our trusted cloud provider. Together, we will reshape the future of retail, making it smarter, faster, and more attuned to the ever-evolving needs of consumers.

Conclusion

In this technical traverse, we’ve highlighted our transformational journey using AWS Inferentia for its innovative AI/ML transactional processing system. This partnership has led to a five times increase in processing speed and a stunning 95 percent reduction in inference costs compared to our previous solution. It has changed the current approach of the retail industry by facilitating a real-time and seamless shopping experience.

If you’re interested in learning more about how Inferentia can help you save costs while optimizing performance for your inference applications, visit the Amazon EC2 Inf1 instances and Amazon EC2 Inf2 instances product pages. AWS provides various sample codes and getting started resources for Neuron SDK that you can find on the Neuron samples repository.

About the Authors

Matias Ponchon is the Head of Infrastructure at Intuitivo. He specializes in architecting secure and robust applications. With extensive experience in FinTech and Blockchain companies, coupled with his strategic mindset, helps him to design innovative solutions. He has a deep commitment to excellence, that’s why he consistently delivers resilient solutions that push the boundaries of what’s possible.

Jose Benitez is the Founder and Director of AI at Intuitivo, specializing in the development and implementation of computer vision applications. He leads a talented Machine Learning team, nurturing an environment of innovation, creativity, and cutting-edge technology. In 2022, Jose was recognized as an ‘Innovator Under 35’ by MIT Technology Review, a testament to his groundbreaking contributions to the field. This dedication extends beyond accolades and into every project he undertakes, showcasing a relentless commitment to excellence and innovation.

Diwakar Bansal is an AWS Senior Specialist focused on business development and go-to-market for Gen AI and Machine Learning accelerated computing services. Previously, Diwakar has led product definition, global business development, and marketing of technology products for IoT, Edge Computing, and Autonomous Driving focusing on bringing AI and Machine Learning to these domains.

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