Data engineering for ML and AI with AWS analytics (ANT405)

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

1. 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.

2. 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.

3. 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.

4. 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

• Amazon Kinesis Data Streams:

  • Aggregate and compress data before writing to streams.
  • Use Enhanced Fan-Out consumers for high-throughput scenarios.

• AWS Glue:

  • Right-size worker types for optimal performance.
  • Use Flex execution for non-time-sensitive jobs.
  • Leverage job bookmarks for incremental processing.
  • Optimize shuffles and use predicate pushdowns.

• Amazon EMR:

  • Use the right instance types for your workloads.
  • Leverage Graviton-based instances for better price-performance.
  • Upgrade to the latest EMR versions for performance improvements.

• Data Quality and Governance:

  • Implement data quality rules using AWS Glue Data Quality.
  • Use tag-based access control in AWS Lake Formation.
  • Provide a business-friendly data catalog using Amazon Data Lens.

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.