In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks.
Machine Learning Modeling Pipelines in Production
This course is part of Machine Learning Engineering for Production (MLOps) Specialization
Taught in English
Some content may not be translated
Instructor: Robert Crowe
31,995 already enrolled
Course
(414 reviews)
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What you'll learn
Apply techniques to manage modeling resources and best serve batch and real-time inference requests.
Use analytics to address model fairness, explainability issues, and mitigate bottlenecks.
Skills you'll gain
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12 quizzes
Course
(414 reviews)
87%
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There are 5 modules in this course
Learn how to effectively search for the best model that will scale for various serving needs while constraining model complexity and hardware requirements.
What's included
10 videos5 readings2 quizzes2 app items1 ungraded lab
Learn how to optimize and manage the compute, storage, and I/O resources your model needs in production environments during its entire lifecycle.
What's included
13 videos7 readings2 quizzes
Implement distributed processing and parallelism techniques to make the most of your computational resources for training your models efficiently.
What's included
6 videos5 readings2 quizzes1 app item
Use model performance analysis to debug and remediate your model and measure robustness, fairness, and stability.
What's included
12 videos6 readings3 quizzes3 ungraded labs
Learn about model interpretability - the key to explaining your model’s inner workings to laypeople and expert audiences and how it promotes fairness and helps address regulatory and legal requirements for different use cases.
What's included
11 videos12 readings3 quizzes1 app item
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