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Validate, Analyze, and Monitor ML Models

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Validate, Analyze, and Monitor ML Models

This course is part of Gradient to Production: MLOps & Model Serving Specialization

Instructor: ansrsource instructors

Included with

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1 module

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

1 module

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

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Build your subject-matter expertise

This course is part of the Gradient to Production: MLOps & Model Serving Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There is 1 module in this course

In this course, The learner 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 like 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, The learner will be ready to support safe model releases and long-term model health.

Module details

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.