Remove Accountability Remove Analysis Remove APIs Remove Scripts
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Configure an AWS DeepRacer environment for training and log analysis using the AWS CDK

AWS Machine Learning

In this post, we enable the provisioning of different components required for performing log analysis using Amazon SageMaker on AWS DeepRacer via AWS CDK constructs. This is where advanced log analysis comes into play. Make sure you have the credentials and permissions to deploy the AWS CDK stack into your account.

Scripts 73
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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Continuous integration and continuous delivery (CI/CD) pipeline – Using the customer’s GitHub repository enabled code versioning and automated scripts to launch pipeline deployment whenever new versions of the code are committed. Wipro has used the input filter and join functionality of SageMaker batch transformation API.

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Simplify continuous learning of Amazon Comprehend custom models using Comprehend flywheel

AWS Machine Learning

Flywheel creates a data lake (in Amazon S3) in your account where all the training and test data for all versions of the model are managed and stored. You can use the flywheel active model version to run custom analysis (real-time or asynchronous jobs). The data can be accessed from AWS Open Data Registry.

APIs 68
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­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

The offline store data is stored in an Amazon Simple Storage Service (Amazon S3) bucket in your AWS account. Customers can also access offline store data using a Spark runtime and perform big data processing for ML feature analysis and feature engineering use cases. You can find the sample script in GitHub. AWS Glue Job setup.

Scripts 73
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GPT-NeoXT-Chat-Base-20B foundation model for chatbot applications is now available on Amazon SageMaker

AWS Machine Learning

As a JumpStart model hub customer, you get improved performance without having to maintain the model script outside of the SageMaker SDK. The inference script is prepacked with the model artifact. nYou can access Amazon Comprehend document analysis capabilities using the Amazon Comprehend console or using the Amazon Comprehend APIs.

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Run inference at scale for OpenFold, a PyTorch-based protein folding ML model, using Amazon EKS

AWS Machine Learning

Model weights are available via scripts in the GitHub repository , and the MSAs are hosted by the Registry of Open Data on AWS (RODA). The MSA step in the protein folding workflow is computationally intensive and can account for a majority of the inference time. Set up the EKS cluster with an FSx for Lustre file system.

APIs 76
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Build a powerful question answering bot with Amazon SageMaker, Amazon OpenSearch Service, Streamlit, and LangChain

AWS Machine Learning

We implement the RAG functionality inside an AWS Lambda function with Amazon API Gateway to handle routing all requests to the Lambda. We implement a chatbot application in Streamlit which invokes the function via the API Gateway and the function does a similarity search in the OpenSearch Service index for the embeddings of user question.

APIs 76