Platform Engineering for Data Science at Scale
summary
Claims concrete outcomes: 3x productivity, 50% faster deployments, and 40% infrastructure cost reduction. Platform uses containerization and microservices with orchestration and service mesh plus event driven pipelines, data lineage, versioning, and autoscaling. Built a self service ML platform with observability for accuracy anomalies and data freshness that integrates CI CD style pipelines and rollback via immutable infrastructure and circuit breakers. Numbers come as explicit ROI claims and practical tooling choices linked in the uploader's abstract.
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chapters (10)
- 0:00Introduction to Platform Engineering for Data Science
- 0:27Challenges in Scaling Data Science
- 2:28Key Principles of Platform Engineering
- 3:15Core Technologies and Design Patterns
- 4:42Building Effective Machine Learning Pipelines
- 5:20Serving Multiple Stakeholders
- 6:00Monitoring and Future Trends
- 7:06Benefits and Strategic Importance
- 8:14Implementation Roadmap
- 8:46Conclusion and Q&A
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