Module 1 · MLOps: Deploying Machine Learning & Deep Learning Models

ML Systems in Production: lessons

Why models degrade after launch, how batch and online inference differ, and what to monitor.

4 lessons · 1h 4m · 1 free to read now

What this module teaches, what it asks of you first and how you practise it are on its syllabus page.

Why Models Decay in Production
18 min
A model is trained on the past and used on the future. When the world shifts, its accuracy falls — often without a single error in the logs.
Free · read nowRead the lesson Simulation inside
Training-Serving Skew
16 min
When features are computed differently in training and at prediction time — the most common production ML bug, and the one least likely to show up in a log.
Included with the track Simulation inside
Batch vs Online Inference
14 min
Scheduled or on-request predictions: this choice decides latency, cost, which features you can use, and how the system fails.
Included with the track
An ML System Design Checklist
16 min
Five questions to answer before a single line of deployment code is written — because the most expensive ML failures are not bugs, but systems built for the wrong question.
Included with the track Simulation inside

Why Models Decay in Production