Introduction to Machine Learning in Production
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Course overview
- Provider
- Coursera
- Course type
- Free online course
- Level
- Advanced
- Deadline
- Flexible
- Duration
- 10 hours
- Certificate
- Paid Certificate Available
- Course author
- Andrew Ng
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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.
Description
In the first course of Machine Learning Engineering for Production Specialization, 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; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills.
Week 1: Overview of the ML Lifecycle and Deployment
Week 2: Selecting and Training a Model
Week 3: Data Definition and Baseline
