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.

Your Digital Journey deserves a great story.

Build one with us.

Cookies Icon

These cookies are used to collect information about how you interact with this website and allow us to remember you. We use this information to improve and customize your browsing experience, as well as for analytics.

If you decline, your information won’t be tracked when you visit this website. A single cookie will be used in your browser to remember your preference.

Talk to us