Monthly Archives: November 2022

Over the last 5 years, artificial intelligence (AI) and machine learning (ML) have evolved from a niche activity to a rapidly growing mainstream endeavor. Today, more than 100,000 customers across numerous industries rely on AWS for ML and AI initiatives that infuse AI into a broad range of business use cases to automate repetitive and mundane tasks—from intelligent demand planning to document processing and content moderation. AWS AI services help customers create smoother, faster, and more efficient engagements with customers, driving greater efficiencies and lowering operational costs.

At AWS re:Invent, Amazon Web Services, Inc. has announced a series of features and enhancements across its portfolio of AI services, including purpose-built solutions to solve industry-specific challenges, representing a deeper integration of AI into everyday experiences. The new capabilities include Amazon Textract Analyze Lending to improve loan-document processing efficiency, Amazon Transcribe Call Analytics to analyze in-progress contact center calls, Amazon Kendra support for tabular search in HTML and seven new languages, Amazon HealthLake Imaging for medical image storing; Amazon HealthLake Analytics with multi-modal data querying capabilities, and broader programming languages support and easier administration in Amazon CodeWhisperer. These AI service innovations provide vertical markets and horizontal functions with deeper, real-time insights and cost-saving efficiencies to drive transformation across industries.

These new capabilities enhance AWS’s AI offerings at the top of its three-layer ML stack. The bottom layer includes foundational components (ML hardware and ML software libraries) to help customers build their own ML infrastructure, and the middle layer—Amazon SageMaker—is a fully managed ML development environment. The top layer of AI services brings ML to business use cases such as transcribing contact center calls, processing documents, and improving healthcare outcomes. Customers can use AWS AI services with no ML expertise required.

Customers from different industries rely on AWS AI services to improve efficiency and reduce operational costs. For example, WaFd Bank, a full-service US bank, improved its customer experience with Talkdesk (a global cloud contact center company) and AWS Contact Center Intelligence (CCI) solutions, reducing call times by up to 90%. And State Auto, a property and casualty insurance holding company, automated the property inspection process using Amazon Rekognition (a computer vision service), increasing the number of claims it reviews for potential fraud by 83%.

Amazon Textract Analyze Lending makes it easy to classify and extract mortgage loan data

Today, mortgage companies process large volumes of documents to extract business-critical data and make decisions on loan applications. For example, a typical US mortgage application can encompass 500 or more pages of diverse document types, including W2 forms, paystubs, bank statements, Form 1040, 1003, and many more. The lender’s loan processing application has to first understand and classify each document type to ensure that it is processed the right way. After that, the loan processing application has to extract all the data on each page of the document. The data in these documents exists in different formats and structures, and the same data element can have different names on different documents—for example, “SSN,” or “Social Security Number,” which can lead to inaccurate data extraction. So far, the classification and extraction of data from mortgage application packages have been primarily manual tasks. Furthermore, mortgage companies have to manage demand for mortgages that can fluctuate substantially during a year, so lenders are unable to plan effectively and must often allocate resources to process documents on an ad hoc basis. Overall, mortgage loan processing is still manual, slow, error-prone, and expensive.

Amazon Textract (AWS’s AI service to automatically extract text, handwriting, and data from scanned documents) now offers Amazon Textract Analyze Lending to make loan document processing more automated, faster, and cost-effective at scale. Amazon Textract Analyze Lending pulls together multiple ML models to classify various documents that commonly occur in mortgage packages, and then extracts critical information from these documents with high accuracy to improve loan document processing workflows. For example, it can now perform signature detection to identify whether documents have required signatures. It also provides a summary of the documents in a mortgage application package and identifies any missing documents. For instance, PennyMac, a financial services firm specializing in the production and servicing of US mortgage loans, uses Amazon Textract Analyze Lending to process a 3,000-page mortgage application in less than 5 minutes. Previously, PennyMac’s mortgage document processing required several hours of reviewing and preparing a loan package for approval.

Amazon Transcribe Call Analytics for improved end-user experiences

In most customer-facing industries such as telecom, finance, healthcare, and retail, customer experiences with call centers can profoundly impact perceptions of the company. Lengthy call-resolution times or the inability to deal with issues during live interactions can lead to poor customer experiences or customer churn. Contact centers need real-time insights into customer-experience issues (e.g., a product defect) while calls are in progress. Typically, developers use multiple AI services to generate live call transcriptions, extract relevant real-time insights, and manage sensitive customer information (e.g. identify and redact sensitive customer details) during live calls. However, this process adds unnecessary complexity, time, and cost.

Amazon Transcribe, an automatic speech recognition (ASR) service that makes it easy for developers to add speech-to-text capabilities to their applications, now supports call analytics to provide real-time conversation insights. Amazon Transcribe Call Analytics now provides real-time conversation insights that help analyze thousands of in-progress calls, identify call sentiment (e.g. calls that ended with a negative customer sentiment score), detect the potential reason for the call, and spot issues such as repeated requests to speak to a manager. Amazon Transcribe Call Analytics combines powerful automatic speech NLP models that are trained specifically to improve overall customer experience. With Amazon Transcribe Call Analytics, developers can build a real-time system that provides contact center agents with relevant information to solve customer issues or alert supervisors about potential issues. Amazon Transcribe Call Analytics also generates call summaries automatically, eliminating the need for agents to take notes and allowing them to focus on customer needs. Furthermore, Amazon Transcribe Call Analytics protects sensitive customer data by identifying and redacting personal information during live calls.

Amazon Kendra adds new search capabilities

Today, in the face of rapid growth in the volume and variety of data, enterprise search tools struggle to examine and uncover key insights stored across enterprise systems in heterogenous data formats and in different languages. Conventional enterprise search solutions are unable to find knowledge stored in unstructured datasets like HTML tables because it requires extracting information from two-dimensional formats (rows and columns). Sometimes, the information a customer may be seeking could exist in different languages, making the search even more challenging. As a result, enterprise employees waste time searching for information or are unable to perform their duties.

Amazon Kendra (AWS’s intelligent search service powered by ML) offers a new capability that supports tabular search in HTML. Customers can find more precise answers faster in HTML documents, whether they’re in the narrative body or tabular form, by using natural language questions. Amazon Kendra can find and extract precise answers from HTML tables by performing deeper analyses of HTML pages and using new specialized deep learning models that intelligently interpret columns and rows to pinpoint relevant data. Amazon Kendra is also adding semantic support for seven new languages (in addition to English): French, Spanish, German, Portuguese, Japanese, Korean, and Chinese. Customers can now ask natural language questions and get exact answers in any of the supported languages. One of AWS’s biopharmaceutical customers, Gilead Sciences Inc., increased staff productivity by cutting internal search times by roughly 50% using Amazon Kendra.

Amazon HealthLake offers next-generation imaging solutions and precision health analytics

Healthcare providers face a myriad of challenges as the scale and complexity of medical imaging data continues to increase. Medical imaging is a critical tool to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% ¹ of all healthcare data, and analyzing these complex images has largely been a manual task performed by experts and specialists. It often takes data scientists and researchers weeks or months to derive important insights from medical images, slowing down decision-making processes for healthcare providers and impacting patient-care delivery. To address these challenges, Amazon HealthLake (a HIPAA-eligible service to store, transform, query, and analyze large-scale health data) is adding two new capabilities for medical imaging and analytics:

• Amazon HealthLake Imaging is a new HIPAA-eligible capability that enables healthcare providers and their software partners to easily store, access, and analyze medical images at petabyte scale. The new capability is designed for fast, subsecond image retrieval in clinical workflows that healthcare providers can access securely from anywhere (e.g., web, desktop, or phone) and with high availability. Typically, health systems store multiple copies of the same imaging data in clinical and research systems, leading to increased storage costs and complexity. Amazon HealthLake Imaging extracts and stores just one copy of the same image to the cloud. Customers can now access existing medical records and run analysis applications from a single encrypted copy of the same data in the cloud with normalized metadata and advanced compression. As a result, Amazon HealthLake Imaging can help providers reduce the total cost of medical imaging storage by up to 40%.
• Amazon HealthLake Analytics is a new HIPAA-eligible capability that makes it easy to query and derive insights from multi-modal health data (e.g., imaging, text, or genetics), at the individual or population levels, with the ability to share data securely across the enterprise. It removes the need for healthcare providers to execute complex data exports and data transformations. Amazon HealthLake Analytics automatically normalizes raw health data from disparate sources (e.g., medical records, health insurance claims, EHRs, or medical devices) into an analytics and interoperable format in minutes. The new capability reduces what would otherwise take months of engineering effort to allow providers to focus on what they do best—delivering patient care.

Amazon CodeWhisperer offers broader support and easier administration

While the cloud has democratized application development through on-demand access to compute, storage, database, analytics, and ML, the traditional process of building software applications in any industry remains time-intensive. Developers must still spend significant time writing repetitive code not directly related to the core problems they want to solve. Even highly experienced developers find it difficult to keep up with multiple programming languages, frameworks, and software libraries, while ensuring they follow correct programming syntax and coding best practices.

Amazon CodeWhisperer (an ML-powered service that generates code recommendations) now supports AWS Builder ID so any developer can sign up securely with just an email address and enable Amazon CodeWhisperer for their IDE within the AWS Toolkit. In addition to Python, Java, and JavaScript, Amazon CodeWhisperer adds support for TypeScript and C# languages to accelerate code development. Also, Amazon CodeWhisperer now makes code recommendations for AWS application programming interfaces (APIs) across its most popular services, including Amazon Elastic Compute Cloud (Amazon EC2), AWS Lambda, and Amazon Simple Storage Service (Amazon S3). Finally, Amazon CodeWhisperer is now available on the AWS Management Console, so any authorized AWS administrator can enable Amazon CodeWhisperer for their organization.

Conclusion

With these new features and capabilities, AWS continues to expand its portfolio of the broadest and deepest set of AI services. AWS also recognizes that as AI-powered use cases become pervasive, it is important that these capabilities are built in a responsible way. AWS is committed to building its services in a responsible manner and supporting customers to help them deploy AI responsibly. By enabling customers to more easily and responsibly add new and expanded AI capabilities to their applications and workflows, AWS is unleashing even greater innovation and helping businesses reimagine how they approach and solve some of their most pressing challenges. To learn more about AWS’s comprehensive approach to responsible AI, visit Responsible use of artificial intelligence and machine learning.

References

¹S. K. Zhou et al., “A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises,” in Proceedings of the IEEE, vol. 109, no. 5, pp. 820-838, May 2021, doi: 10.1109/JPROC.2021.3054390.

About the Author

Bratin Saha is the Vice President of Artificial Intelligence and Machine Learning at AWS.

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In 2019, Amazon co-founded the climate pledge. The pledge’s goal is to achieve net zero carbon by 2040. This is 10 years earlier than the Paris agreement outlines. Companies who sign up are committed to regular reporting, carbon elimination, and credible offsets. At the time of this writing, 377 companies have signed the climate pledge, and the number is still growing.

Because AWS is committed to helping you achieve your net zero goal through cloud solutions and machine learning (ML), many projects have already been developed and deployed that reduce carbon emissions. Manufacturing is one of the industries that can benefit greatly from such projects. Through optimized energy management of machines in manufacturing factories, such as compressors or chillers, companies can reduce their carbon footprint with ML.

Effectively transitioning from an ML experimentation phase to production is challenging. Automating model training and retraining, having a model registry, and tracking experiments and deployment are some of the key challenges. For manufacturing companies, there is another layer of complexity, namely how these deployed models can run at the edge.

In this post, we address these challenges by providing a machine learning operations (MLOps) template that hosts a sustainable energy management solution. The solution is agnostic to use cases, which means you can adapt it for your use cases by changing the model and data. We show you how to integrate models in Amazon SageMaker Pipelines, a native workflow orchestration tool for building ML pipelines, which runs a training job and optionally a processing job with a Monte Carlo Simulation. Experiments are tracked in Amazon SageMaker Experiments. Models are tracked and registered in the Amazon SageMaker model registry. Finally, we provide code for deployment of your final model in an AWS Lambda function.

Lambda is a compute service that lets you run code without managing or provisioning servers. Lambda’s automatic scaling, pay-per-request billing, and ease of use make it a common deployment choice for data science teams. With this post, data scientists can turn their model into a cost-effective and scalable Lambda function. Furthermore, Lambda allows for integration with AWS IoT Greengrass, which helps you build software that enables your devices to act at the edge on the data that they generate, as would be the case for a sustainable energy management solution.

Solution overview

The architecture we deploy (see the following figure) is a fully CI/CD-driven approach to machine learning. Elements are decoupled to avoid having one monolithic solution.

Let’s start with the top left of the diagram. The Processing – Image build component is a CI/CD-driven AWS CodeCommit repository that helps build and push a Docker container to Amazon Elastic Container Registry (Amazon ECR). This processing container serves as the first step in our ML pipeline, but it’s also reused for postprocessing steps. In our case, we apply a Monte Carlo Simulation as postprocessing. The Training – Image build repository outlined on the bottom left has the same mechanism as the Processing block above it. The main difference is that it builds the container for model training.

The main pipeline, Model building (Pipeline), is another CodeCommit repository that automates running your SageMaker pipelines. This pipeline automates and connects the data preprocessing, model training, model metrics tracking in SageMaker Experiments, data postprocessing, and, model cataloging in SageMaker model registry.

The final component is on the bottom right: Model deployment. If you follow the examples in Amazon SageMaker Projects, you get a template that hosts your model using a SageMaker endpoint. Our deployment repository instead hosts the model in a Lambda function. We show an approach for deploying the Lambda function that can run real-time predictions.

Prerequisites

To deploy our solution successfully, you need the following:

• An AWS account.
• An AWS Cloud Development Kit (AWS CDK) installation. For this post, we use Python.
• An AWS Command Line Interface (AWS CLI) installation.
• An Amazon SageMaker Studio domain with a user set up.

Download the GitHub repository

As a first step, clone the GitHub repository to your local machine. It contains the following folder structure:

• deployment – Contains code relevant for deployment
• mllib — Contains ML code for preprocessing, training, serving, and simulating
• tests — Contains unit and integration tests

The key file for deployment is the shell script deployment/deploy.sh. You use this file to deploy the resources in your account. Before we can run the shell script, complete the following steps:

1. Open the deployment/app.py and change the bucket_name under SageMakerPipelineSourceCodeStack. The bucket_name needs to be globally unique (for example, add your full name).
2. In deployment/pipeline/assets/modelbuild/pipelines/energy_management/pipeline.py, change the default_bucket under get_pipeline to the same name as specified in step 1.

Deploy solution with the AWS CDK

First, configure your AWS CLI with the account and Region that you want to deploy in. Then run the following commands to change to the deployment directory, create a virtual environment, activate it, install the required pip packages specified in setup.py, and run the deploy.sh:

cd deployment
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
pre-commit install
chmod u+x deploy.sh
./deploy.sh

deploy.sh performs the following actions:

1. Creates a virtual environment in Python.
2. Sources the virtual environment activation script.
3. Installs the AWS CDK and the requirements outlined in setup.py.
4. Bootstraps the environment.
5. Zips and copies the necessary files that you developed, such as your mllib files, into the corresponding folders where these assets are needed.
6. Runs cdk deploy —require-approval never.
7. Creates an AWS CloudFormation stack through the AWS CDK.

The initial stage of the deployment should take less than 5 minutes. You should now have four repositories in CodeCommit in the Region you specified through the AWS CLI, as outlined in the architecture diagram. The AWS CodePipeline pipelines are run simultaneously. The modelbuild and modeldeploy pipelines depend on a successful run of the processing and training image build. The modeldeploy pipeline depends on a successful model build. The model deployment should be complete in less than 1.5 hours.

Clone the model repositories in Studio

To customize the SageMaker pipelines created through the AWS CDK deployment in the Studio UI, you first need to clone the repositories into Studio. Launch the system terminal in Studio and run the following commands after providing the project name and ID:

git clone https://git-codecommit.REGION.amazonaws.com/v1/repos/sagemaker-PROJECT_NAME-PROJECT_ID-modelbuild
git clone https://git-codecommit.REGION.amazonaws.com/v1/repos/sagemaker-PROJECT_NAME-PROJECT_ID-modeldeploy
git clone https://git-codecommit.REGION.amazonaws.com/v1/repos/sagemaker-PROJECT_NAME-PROJECT_ID-processing-imagebuild
git clone https://git-codecommit.REGION.amazonaws.com/v1/repos/sagemaker-PROJECT_NAME-PROJECT_ID-training-imagebuild

After cloning the repositories, you can push a commit to the repositories. These commits trigger a CodePipeline run for the related pipelines.

You can also adapt the solution on your local machine and work on your preferred IDE.

Navigate the SageMaker Pipelines and SageMaker Experiments UI

A SageMaker pipeline is a series of interconnected steps that are defined using the Amazon SageMaker Python SDK. This pipeline definition encodes a pipeline using a Directed Acyclic Graph (DAG) that can be exported as a JSON definition. To learn more about the structure of such pipelines, refer to SageMaker Pipelines Overview.

Navigate to SageMaker resources pane and choose the Pipelines resource to view. Under Name, you should see PROJECT_NAME-PROJECT_ID. In the run UI, there should be a successful run that is expected to take a little over 1 hour. The pipeline should look as shown in the following screenshot.

The run was automatically triggered after the AWS CDK stack was deployed. You can manually invoke a run by choosing Create execution. From there you can choose your own pipeline parameters such as the instance type and number of instances for the processing and training steps. Furthermore, you can give the run a name and description. The pipeline is highly configurable through pipeline parameters that you can reference and define throughout your pipeline definition.

Feel free to start another pipeline run with your parameters as desired. Afterwards, navigate to the SageMaker resources pane again and choose Experiments and trials. There you should again see a line with a name such as PROJECT_NAME-PROJECT_ID. Navigate to the experiment and choose the only run with a random ID. From there, choose the SageMaker training job to explore the metrics related to the training Job.

The goal of SageMaker Experiments is to make it as simple as possible to create experiments, populate them with trials, and run analytics across trials and experiments. SageMaker Pipelines are closely integrated with SageMaker Experiments, and by default for each run create an experiment, trial and trial components in case they do not exist.

Approve Lambda deployment in the model registry

As a next step, navigate to the model registry under SageMaker resources. Here you can find again a line with a name such as PROJECT_NAME-PROJECT_ID. Navigate to the only model that exists and approve it. This automatically deploys the model artifact in a container in Lambda.

After you approve your model in the model registry, an Amazon EventBridge event rule is triggered. This rule runs the CodePipeline pipeline with the ending *-modeldeploy. In this section, we discuss how this solution uses the approved model and hosts it in a Lambda function. CodePipeline takes the existing CodeCommit repository also ending with *-modeldeploy and uses that code to run in CodeBuild. The main entry for CodeBuild is the buildspec.yml file. Let’s look at this first:

version: 0.2

env:
  shell: bash

phases:
  install:
    runtime_versions:
      python: 3.8
    commands:
      - python3 -m ensurepip --upgrade
      - python3 -m pip install --upgrade pip
      - python3 -m pip install --upgrade virtualenv
      - python3 -m venv .venv
      - source .venv/bin/activate
      - npm install -g aws-cdk@2.26.0
      - pip install -r requirements.txt
      - cdk bootstrap
  build:
    commands:
      - python build.py --model-package-group-name "$SOURCE_MODEL_PACKAGE_GROUP_NAME"
      - tar -xf model.tar.gz
      - cp model.joblib lambda/digital_twin
      - rm model.tar.gz
      - rm model.joblib
      - cdk deploy --require-approval never

During the installation phase, we ensure that the Python libraries are up to date, create a virtual environment, install AWS CDK v2.26.0, and install the aws-cdk Python library along with others using the requirements file. We also bootstrap the AWS account. In the build phase, we run build.py, which we discuss next. That file downloads the latest approved SageMaker model artifact from Amazon Simple Storage Service (Amazon S3) to your local CodeBuild instance. This .tar.gz file is unzipped and its contents copied into the folder that also contains our main Lambda code. The Lambda function is deployed using the AWS CDK, and code runs out of a Docker container from Amazon ECR. This is done automatically by AWS CDK.

The build.py file is a Python file that mostly uses the AWS SDK for Python (Boto3) to list the model packages available.

The function get_approved_package returns the Amazon S3 URI of the artifact that is then downloaded, as described earlier.

After successfully deploying your model, you can test it directly on the Lambda console in the Region you chose to deploy in. The name of the function should contain DigitalTwinStack-DigitalTwin*. Open the function and navigate to the Test tab. You can use the following event to run a test call:

{
  "flow": "[280, 300]",
  "pressure": "[69, 70]",
  "simulations": "10",
  "no_of_trials": "10",
  "train_error_weight": "1.0"
}

After running the test event, you get a response similar to that shown in the following screenshot.

If you want to run more simulations or trials, you can increase the Lambda timeout limit and experiment with the code! Or you might want to pick up the data generated and visualize the same in Amazon QuickSight. Below is an example. It’s your turn now!

Clean up

To avoid further charges, complete the following steps:

• On the AWS CloudFormation console, delete the EnergyOptimization stack.
  This deletes the entire solution.
• Delete the stack DigitalTwinStack, which deployed your Lambda function.

Conclusion

In this post, we showed you a CI/CD-driven MLOps pipeline of an energy management solution where we keep each step decoupled. You can track your ML pipelines and experiments in the Studio UI. We also demonstrated a different deployment approach: upon approval of a model in the model registry, a Lambda function hosting the approved model is built automatically through CodePipeline.

If you’re interested in exploring either the MLOps pipeline on AWS or the sustainable energy management solution, check out the GitHub repository and deploy the stack in your own AWS environment!

About the Authors

Laurens van der Maas is a Data Scientist at AWS Professional Services. He works closely with customers building their machine learning solutions on AWS, and is passionate about how machine learning is changing the world as we know it.

Kangkang Wang is an AI/ML consultant with AWS Professional Services. She has extensive experience of deploying AI/ML solutions in healthcare and life sciences vertical. She also enjoys helping enterprise customers to build scalable AI/ML platforms to accelerate the cloud journey of their data scientists.

Selena Tabbara is a Data Scientist at AWS Professional Services. She works everyday with her customers to achieve their business outcomes by innovating on AWS platforms. In her spare time, Selena enjoys playing the piano, hiking and watching basketball.

Michael Wallner is a Senior Consultant with focus on AI/ML with AWS Professional Services. Michael is passionate about enabling customers on their cloud journey to become AWSome. He is excited about manufacturing and enjoys helping transform the manufacturing space through data.

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