- Concept Drift
- ML Deployment Challenges
- Human-level Performance (HLP)
- Project Scoping and Design
- Model baseline
Introduction to Machine Learning in Production
Completed by Ricardo Rossiter Barioni
September 3, 2021
11 hours (approximately)
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What you will learn
Identify the key components of the ML lifecycle and pipeline and compare the ML modeling iterative cycle with the ML product deployment cycle.
Understand how performance on a small set of disproportionately important examples may be more crucial than performance on the majority of examples.
Solve problems for structured, unstructured, small, and big data. Understand why label consistency is essential and how you can improve it.
Skills you will gain
