This intermediate-level course is designed for machine learning engineers, data scientists, and ML Ops practitioners who are responsible for releasing and maintaining models in production. Building a model is only the beginning. To deliver reliable business value, models must be validated on unseen data, compared against baselines in live environments, and continuously monitored for drift.
Validate, Analyze, and Monitor ML Models
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
3 hours to complete
Flexible schedule
Learn at your own pace
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
March 2026
Taught in English
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There is 1 module in this course
This intermediate-level course is designed for machine learning engineers, data scientists, and ML Ops practitioners who are responsible for releasing and maintaining models in production. Building a model is only the beginning. To deliver reliable business value, models must be validated on unseen data, compared against baselines in live environments, and continuously monitored for drift. In this course, learners will learn how to validate release candidates using hold-out datasets, analyze A/B test and shadow deployment results to quantify performance improvements, and monitor data and prediction drift using practical indicators, such as PSI. Through short videos, guided coach conversations, and hands-on learning activities, I will practice decision-making that mirrors real production workflows. By the end, learners will be ready to support safe model releases and long-term model health.
What's included
7 videos3 readings3 assignments1 ungraded lab
Instructor
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