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.
This course is part of the Machine Learning Engineering for Production (MLOps) Specialization
Offered By

About this Course
• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
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
- Human-level Performance (HLP)
- Concept Drift
- Model baseline
- Project Scoping and Design
- ML Deployment Challenges
• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Offered by
Syllabus - What you will learn from this course
Week 1: Overview of the ML Lifecycle and Deployment
Week 2: Select and Train a Model
Week 3: Data Definition and Baseline
Reviews
- 5 stars84.42%
- 4 stars12.94%
- 3 stars1.91%
- 2 stars0.42%
- 1 star0.29%
TOP REVIEWS FROM INTRODUCTION TO MACHINE LEARNING IN PRODUCTION
I really enjoy participating in a great class like Andrew's class. It's full of useful and applicable points that I encounter during a real prj.
Thanks for sharing this asset with us :))
Good intro on key concept in MLOps. Would recommend it to anyone who is stepping into this field as well as for ML Hobbists to understand the main challenges of a ML production system
This is a great course to learn many practical procedures and techniques, to apply ML algorithms to real world problems and do it well, by avoiding common mistakes and deliver value.
Practical and well-structured advices throughout the lifecycle of ML. Examples from real world problems & experiences make the advices more tangible and helps to reflect on own problems.
About the Machine Learning Engineering for Production (MLOps) Specialization

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