- Concept Drift
- ML Deployment Challenges
- Human-level Performance (HLP)
- Project Scoping and Design
- Model baseline
Machine Learning in Production
Completed by Ricardo Rossiter Barioni
September 3, 2021
11 hours (approximately)
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What you will learn
Identify key components of the ML project lifecycle, pipeline & select the best deployment & monitoring patterns for different production scenarios.
Optimize model performance and metrics by prioritizing disproportionately important examples that represent key slices of a dataset.
Solve production challenges regarding structured, unstructured, small, and big data, how label consistency is essential, and how you can improve it.
Skills you will gain
