Smarter, faster Data analytics with generative AI and Machine Learning
summary
Shows a concrete pipeline: using Amazon Q for data integration to turn English into AWS Glue jobs that join S3/RDS/Redshift sources and generate optimized code, plus an AWS Glue data quality engine that uses ML to learn patterns (New York Taxi example) and recommend dynamic rules instead of static thresholds. Covers generative SQL into Amazon Redshift, autonomics that do automatic table optimization, workload management and materialized views and claims up to 40% performance improvements from those optimizations.
AI-written summary · about
chapters (14)
- 0:00Generative AI in data analytics
- 1:27What generative AI is
- 2:29Scalable data infrastructure
- 3:57Introduction
- 4:41Data integration challenges
- 6:07Pipelines with natural language
- 7:33Data quality with ML
- 9:43Demo: data quality ML
- 11:07Generative AI and ML in Redshift
- 13:13Autonomics for query performance
- 16:01Materialized views in Redshift
- 17:08Generative AI in QuickSight
- 19:16Creating data stories
- 19:59Summary
related talks
19mHow to use generative AI to deliver self-service data products
44mBest practices for analytics and generative AI on AWS(ANT302)
01h 13mDriving business with autonomous analytics and data democratization
52mPreparing data for reporting, ML, and AI on AWS
43mData warehousing reinvented for today's needs
36mNew trends in data modernization: Catalysts for AI/ML analytics on AWS
28mML + Big Data: Spark on Amazon SageMaker with Amazon Glue
23mGenerative AI use cases