Making Models Work: How an optimized ML lifecycle can drive better ROI
summary
Shows a platform play with concrete results: a bank uses 2 PB of data to cut AML false positives up to 60% and then scales the same pipelines to 25 teams for a 40% operational efficiency lift; a telco ingests ~0.5 PB daily and runs churn/cross-sell over ~93M customers. Covers tooling: hybrid compute on CDP, Cloudera SDX lineage and governance, containerized Spark + Airflow data engineering, model packaging/monitoring, an amps catalog of end-to-end prototypes and CML drag-and-drop dashboards for citizen analysts. Choose if ROI from reuse, lineage, and ops-ready ML matters.
AI-written summary · about
chapters (17)
- 0:00Introduction and trivia
- 1:13AI as islands not platforms
- 3:20Customer success with unified ML
- 4:23Data lifecycle in ML projects
- 5:28Hybrid data and compute platform
- 6:31Three personas in ML lifecycle
- 7:57Data scientist needs and tools
- 9:18Repetitive ML use cases
- 10:23Applied ML prototypes
- 13:13Scaling model deployments
- 15:18Data engineering service
- 18:09Model operations and governance
- 19:55Business user integration
- 21:43AI-powered visual analytics
- 23:29Customer example Globe Telecom
- 24:54ROI from platform ML
- 25:37Q&A session
related talks
22mAccelerating ML impact
27mA Look Inside Applied Machine Learning
13mMLOps at Scale
25mBuilding Scalable MLOps Pipelines That Actually Work
10mOptimizing Machine Learning (ML Ops) in Financial Services with Kubernetes and Cloud Technologies.
16mOperationalizing AI in Finance: Real-Time Analytics
41mInfrastructure for ML Applications
26mManaging ML Models @ Scale