
Course 1: Introduction to Machine Learning in Production
In this course, you will:
– Identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements.
– Establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.
Week 1: Overview of the ML Lifecycle and Deployment
- Identify the key components of the ML lifecycle and pipeline and compare the ML modeling iterative cycle with the ML product deployment cycle
Week 2: Selecting and Training a Model
- Identify the key challenges in model development and understand how performance on a small set of disproportionately important examples may be more crucial than performance on the majority of examples
Week 3: Data Definition and Baseline
- Compare the various types of problems to be solved for structured vs. unstructured data and big vs. small data and understand why label consistency is essential and how you can improve it