# AI & machine learning
<a name="machinelearning-pattern-list"></a>

**Topics**
+ [Associate an AWS CodeCommit repository in one AWS account with Amazon SageMaker AI Studio Classic in another account](associate-an-aws-codecommit-repository-in-one-aws-account-with-sagemaker-studio-in-another-account.md)
+ [Automatically extract content from PDF files using Amazon Textract](automatically-extract-content-from-pdf-files-using-amazon-textract.md)
+ [Build a cold start forecasting model by using DeepAR for time series in Amazon SageMaker AI Studio Lab](build-a-cold-start-forecasting-model-by-using-deepar.md)
+ [Build an MLOps workflow by using Amazon SageMaker AI and Azure DevOps](build-an-mlops-workflow-by-using-amazon-sagemaker-and-azure-devops.md)
+ [Configure model invocation logging in Amazon Bedrock by using AWS CloudFormation](configure-bedrock-invocation-logging-cloudformation.md)
+ [Create a custom Docker container image for SageMaker and use it for model training in AWS Step Functions](create-a-custom-docker-container-image-for-sagemaker-and-use-it-for-model-training-in-aws-step-functions.md)
+ [Use Amazon Bedrock agents to automate creation of access entry controls in Amazon EKS through text-based prompts](using-amazon-bedrock-agents-to-automate-creation-of-access-entry-controls-in-amazon-eks.md)
+ [Deploy a RAG use case on AWS by using Terraform and Amazon Bedrock](deploy-rag-use-case-on-aws.md)
+ [Deploy preprocessing logic into an ML model in a single endpoint using an inference pipeline in Amazon SageMaker](deploy-preprocessing-logic-into-an-ml-model-in-a-single-endpoint-using-an-inference-pipeline-in-amazon-sagemaker.md)
+ [Deploy real-time coding security validation by using an MCP server with Kiro and other coding assistants](deploy-real-time-coding-security-validation-by-using-an-mcp-server-with-kiro-and-other-coding-assistants.md)
+ [Develop advanced generative AI chat-based assistants by using RAG and ReAct prompting](develop-advanced-generative-ai-chat-based-assistants-by-using-rag-and-react-prompting.md)
+ [Develop a fully automated chat-based assistant by using Amazon Bedrock agents and knowledge bases](develop-a-fully-automated-chat-based-assistant-by-using-amazon-bedrock-agents-and-knowledge-bases.md)
+ [Document institutional knowledge from voice inputs by using Amazon Bedrock and Amazon Transcribe](document-institutional-knowledge-from-voice-inputs-by-using-amazon-bedrock-and-amazon-transcribe.md)
+ [Generate personalized and re-ranked recommendations using Amazon Personalize](generate-personalized-and-re-ranked-recommendations-using-amazon-personalize.md)
+ [Streamline machine learning workflows from local development to scalable experiments by using SageMaker AI and Hydra](streamline-machine-learning-workflows-by-using-amazon-sagemaker.md)
+ [Translate natural language into query DSL for OpenSearch and Elasticsearch queries](translate-natural-language-query-dsl-opensearch-elasticsearch.md)
+ [Use Amazon Q Developer as a coding assistant to increase your productivity](use-q-developer-as-coding-assistant-to-increase-productivity.md)
+ [Use SageMaker Processing for distributed feature engineering of terabyte-scale ML datasets](use-sagemaker-processing-for-distributed-feature-engineering-of-terabyte-scale-ml-datasets.md)
+ [Visualize AI/ML model results using Flask and AWS Elastic Beanstalk](visualize-ai-ml-model-results-using-flask-and-aws-elastic-beanstalk.md)
+ [More patterns](machinelearning-more-patterns-pattern-list.md)