One Flow to Rule Them All: Building the end to end ML project to production
summary
Demos building an end-to-end ML flow inside Dataiku DSS that connects 40+ data sources, mixes visual recipes and code notebooks, trains models with automl or custom Python/R, versions models, and deploys them as endpoints via an API deployer. Shows scenario automation for scheduled or data-change triggers, A/B routing, model and data-drift checks, RBAC and catalog reuse, and managed folders for models. Cites industry pain: 87% of projects fail to impact and 75% fear falling behind without AI.
AI-written summary · about
chapters (7)
- 0:00Introduction to DataC DSS
- 2:47Path to Enterprise AI
- 7:40Fast track to value
- 14:19Durable AI resilience
- 23:01Company transformation with AI
- 50:21End-to-end ML flow demo
- 1:01:19Q&A session
related talks
43mMachine Learning Engineering principles with Python and MLFlow
37mPlatform for Complete Machine Learning Lifecycle
24mFrom Model to Production: Automating the ML Lifecycle with CI/CD
31mMLflow: Platform for Complete Machine Learning Lifecycle
57mManaging Machine Learning Lifecycle with MLflow
57mDevOps for Machine Learning
14mGetting Machine Learning applications into production using MLOps and CD4ML
01h 29mFull-stack Machine Learning and Generative AI for Data Scientists