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.