Machine Learning in Production

What you’ll learn

1. Identify key components of the ML project lifecycle, pipeline & select the best deployment & monitoring patterns for different production scenarios.
2. Optimize model performance and metrics by prioritizing disproportionately important examples that represent key slices of a dataset.
3. Solve production challenges regarding structured, unstructured, small, and big data, how label consistency is essential, and how you can improve it.