Here is a detailed summary of the key takeaways from the session in markdown format:
Data Engineering for ML and AI with AWS Analytics
Importance of Data Strategy for AI/ML Success
- Availability of high-quality data is crucial for successful AI/ML applications and providing personalized customer experiences.
- Building a comprehensive data strategy is key to ensuring data is available, accessible, and governed for AI/ML use cases.
Building a Data Strategy using AWS Analytics Services
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Data Ingestion:
- Use AWS Glue, Amazon MSK, and AWS Data Sync to ingest data from diverse sources (batch, streaming, on-premises).
- Ingest data in raw format for future reprocessing needs.
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Data Processing and Transformation:
- Leverage AWS Glue or Amazon EMR for ETL and data transformations.
- Leverage AWS Glue's built-in data quality features to ensure data quality.
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Data Cataloging and Governance:
- Catalog data using AWS Glue Data Catalog.
- Implement fine-grained access control using AWS Lake Formation.
- Provide a business-friendly data catalog using Amazon Data Lens.
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Data Consumption:
- Use AWS Glue, Amazon EMR, or Amazon SageMaker Data Wrangler for data processing and feature engineering.
- Store vector data in Amazon Aurora or Amazon OpenSearch for Gen AI use cases.
- Train models using Amazon SageMaker or Amazon Bedrock for Gen AI applications.
- Leverage Amazon DocumentDB or Amazon DynamoDB to maintain session information and context.
Best Practices for Leveraging AWS Services
Leveraging Structured Data for Gen AI Applications
- Translating natural language to SQL queries is the equivalent of retrieval-augmented generation (RAG) for structured data.
- Challenges include personalization to the schema, handling different SQL dialects, and dealing with ambiguous column names.
- Amazon Bedrock now offers a new service called Amazon Bedrock Knowledge Base for Structured Data Stores to simplify this process.
Next Thing's Journey with Gen AI and AWS
- Next Thing built a data platform leveraging AWS services like Amazon MSK, Amazon EKS, and Amazon Bedrock.
- Key principles:
- Leverage managed services, asynchronous communication, and microservices.
- Ensure high resiliency and scalability.
- Challenges and solutions:
- Handling high data volumes and throughput in Amazon MSK.
- Implementing pre-processing and fine-tuning of language models for better accuracy.
- Centralizing data in a data lake while respecting data locality requirements.