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MLOps: Deploying Machine Learning & Deep Learning Models

Monitoring and Retraining

Drift detection, performance with delayed labels, retraining strategies, and safe rollouts.

Level
Intermediate
Lessons
3
Total time
48 min
Simulations
4
Included in MLOps: Deploying Machine Learning & Deep Learning Models · $9
Tools you'll use
  • Python
  • Docker
  • FastAPI
  • Kubernetes
  • GitHub
Skills
  • Drift detection
  • Shadow deployment
  • Model A/B testing
Try it now — free

This is the real simulation from the module. No account needed.

What you will be able to do

  • Build a model monitoring dashboard
  • Release a new model without taking a big risk

Before you start

A suggestion, never a lock — you can start here.

  • ml-systems-in-production
Modules that help first

Syllabus

3 lessons · 48 min

Every lesson's title and summary is public. The lessons themselves open once you own the track.

Every lesson in MLOps: Deploying Machine Learning & Deep Learning Models

  1. Detecting Data Drift

    18 min

    PSI with quantile bins, the KS test, why p-values are always "significant" on big data, and how to calibrate thresholds so the alarm does not fire every morning.

    Included with the track Simulation

  2. Retraining Strategies

    15 min

    When to retrain — on a schedule, on drift, or on performance — on which data window, and why a trigger is not permission to release.

    Included with the track Simulation

  3. Shadow and Canary Deployments

    15 min

    Running a new model beside the old one — with no consequences (shadow), then on a small slice of traffic (canary) — and computing how much traffic it takes before the result can be trusted.

    Included with the track Simulation
Lifetime access
$9
Buy the track
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ML Pipelines and CI/CD

Experiment tracking, model registries, and automated pipelines from raw data to a released model.

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Included in MLOps: Deploying Machine Learning & Deep Learning Models· $9