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URL: https://www.coursera.org/learn/validate-analyze-and-monitor-ml-models

⇱ Validate, Analyze, and Monitor ML Models | Coursera

Validate, Analyze, and Monitor ML Models

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Validate, Analyze, and Monitor ML Models

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace
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

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Recently updated!

March 2026

Assessments

3 assignments¹

AI Graded see disclaimer
Taught in English

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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.
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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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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, 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.

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

7 videos•Total 25 minutes
  • Why Validation Is a Release Gate•3 minutes
  • Hold-Out Sets and Evaluation Metrics in Practice•3 minutes
  • From Offline Metrics to Online Impact•4 minutes
  • A/B Tests vs. Shadow Deployments Explained•4 minutes
  • Why Models Drift in Production•4 minutes
  • Using PSI for Ongoing Monitoring•4 minutes
  • Congratulations and Continuous Learning•3 minutes
3 readings•Total 30 minutes
  • Designing a Validation Checklist for Release Candidates•10 minutes
  • Comparing Models Using A/B Testing and Shadow Deployments •10 minutes
  • Automating Monitoring and Retraining Triggers•10 minutes
3 assignments•Total 50 minutes
  • Graded Quiz: Validate, Analyze, and Monitor ML Models•20 minutes
  • Hands-On Activity: Validate a Release Candidate Model•15 minutes
  • Hands-On Activity: Analyze Shadow Deployment Results•15 minutes
1 ungraded lab•Total 60 minutes
  • Build a Drift Monitoring Workflow•60 minutes

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Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.