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 stars84.44%
- 4 stars12.91%
- 3 stars1.91%
- 2 stars0.42%
- 1 star0.29%
TOP REVIEWS FROM INTRODUCTION TO MACHINE LEARNING IN PRODUCTION
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.
Good refresher if you already work in ML. A bit longish and could have been shortened.
I found the code provided useful to remind the use of Keras
In short, solid but not super mandatory
Excellent course, as always! Many thanks!
Great combination of theory + notebooks with practical examples.
Everything is perfectly structured. I will recommend this course to everyone!
The course helped both validate what I knew about the topic and update me about many new trends/tools via high quality references + first hand experences from the instructor.
About the Machine Learning Engineering for Production (MLOps) Specialization

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