Preparing data for reporting, ML, and AI on AWS
summary
Describes using Matillion as an ELT layer on AWS to extract from RDS, stage on S3, and transform inside Amazon Redshift so models and services like SageMaker and Amazon Personalize ingest ready features; demonstrates automated jobs, shared jobs for reusable pipelines, and a real use case loading hundreds of thousands of bounce records; claims 75% faster onboarding for customers after replacing bespoke Java code, uses MD5 blind hashing for privacy, and reports production pipelines that reliably feed ML.
AI-written summary · about
chapters (5)
- 0:00Introduction and overview
- 11:11Clutch’s data challenges
- 29:38Data transformation demo
- 41:25Machine learning tools on AWS
- 46:32Clutch’s next steps and Q&A
related talks
31mProductizing Machine Learning over Big Data with AWS tools
32mMaking Happy Modelers: Build and Maintain...
29mHow financial services companies derive greater value from data with AWS
25mDesigning an AWS Well Architected Framework for AI/ML
28mTurn Data Into Insights
21mSmarter, faster Data analytics with generative AI and Machine Learning
29mData foundation for analytics and AI
44mBest practices for analytics and generative AI on AWS(ANT302)