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Few-shot prompt engineering and fine-tuning for LLMs in Amazon Bedrock

AWS Machine Learning

Earnings calls are live conferences where executives present an overview of results, discuss achievements and challenges, and provide guidance for upcoming periods. Traditionally, earnings call scripts have followed similar templates, making it a repeatable task to generate them from scratch each time.

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Building scalable, secure, and reliable RAG applications using Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Here are some features which we will cover: AWS CloudFormation support Private network policies for Amazon OpenSearch Serverless Multiple S3 buckets as data sources Service Quotas support Hybrid search, metadata filters, custom prompts for the RetreiveAndGenerate API, and maximum number of retrievals.

APIs 127
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How LotteON built a personalized recommendation system using Amazon SageMaker and MLOps

AWS Machine Learning

The main AWS services used are SageMaker, Amazon EMR , AWS CodeBuild , Amazon Simple Storage Service (Amazon S3), Amazon EventBridge , AWS Lambda , and Amazon API Gateway. Real-time recommendation inference The inference phase consists of the following steps: The client application makes an inference request to the API gateway.

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Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock

AWS Machine Learning

For text generation, Amazon Bedrock provides the RetrieveAndGenerate API to create embeddings of user queries, and retrieves relevant chunks from the vector database to generate accurate responses. Boto3 makes it straightforward to integrate a Python application, library, or script with AWS services.

Chatbots 116
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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

The Retrieve and RetrieveAndGenerate APIs allow your applications to directly query the index using a unified and standard syntax without having to learn separate APIs for each different vector database, reducing the need to write custom index queries against your vector store.

APIs 134
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Run text generation with GPT and Bloom models on Amazon SageMaker JumpStart

AWS Machine Learning

We explore two ways of obtaining the same result: via JumpStart’s graphical interface on Amazon SageMaker Studio , and programmatically through JumpStart APIs. The following sections provide a step-by-step demo to perform inference, both via the Studio UI and via JumpStart APIs. JumpStart overview. Solution overview.

APIs 96
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Securing MLflow in AWS: Fine-grained access control with AWS native services

AWS Machine Learning

In this post, we address these limitations by implementing the access control outside of the MLflow server and offloading authentication and authorization tasks to Amazon API Gateway , where we implement fine-grained access control mechanisms at the resource level using Identity and Access Management (IAM). Adds an IAM authorizer.

APIs 82