Deploying & Serving ML Models

What you will be able to do

• Package a trained model as a reproducible, versioned artifact using a serving format such as ONNX, TorchScript, or a framework SavedModel, with dependencies pinned.
• Containerize a model and publish it to a model registry so any environment can pull an identical, traceable deployable.
• Decide between batch and real-time serving for a given workload and defend the choice on latency, cost, and data freshness.
• Build a production model API with a versioned request and response contract, input validation, health checks, and clear error behavior.
• Scale inference to hit a stated latency budget under load using dynamic batching, autoscaling, and the right CPU or GPU hardware.
• Roll out new model versions safely with canary, shadow, and blue-green deployments, and roll back cleanly when a release goes wrong.
• Instrument a served model to detect data drift and concept drift and to track live prediction quality even when ground truth arrives late.
• Set alerting thresholds and retraining triggers that close the loop from a monitoring signal back to a new, tested deployment.

What is inside

4 modules, 8 lessons. Each module ends in a graded quiz and most carry an assignment.

1. 01

   Packaging Models for Production

   Turn a trained model into a reproducible, versioned artifact that any environment can pull and run. This module covers serialization and serving formats (pickle, ONNX, TorchScript, SavedModel) and when each is safe, then the packaging chain of dependency pinning, containers, and model registries that makes a deployable traceable back to the data and code that produced it.

   2 lessons · 5 quiz questions

2. 02

   Serving for Batch and Real-Time Inference

   Batch and online serving solve different problems, and forcing one workload into the other's shape either wastes money or blows the latency budget. This module gives you a decision framework across latency, cost, and freshness, then teaches you to build a model API that other systems can call reliably: transport choice, request and response contracts, feature retrieval, and graceful handling of validation, versioning, and failure.

   2 lessons · 5 quiz questions · assignment

3. 03

   Scaling and Safe Rollouts

   Serving one request on your laptop is easy; serving thousands per second inside a latency budget, on a fixed cost budget, is the job. This module teaches the levers that turn a working endpoint into a scalable service: dynamic batching, autoscaling, response caching, and choosing CPU versus GPU. It then shows how to change a live model without hurting users, using canary, shadow, and blue-green rollouts, each backed by a rollback path you have actually tested.

   2 lessons · 5 quiz questions

4. 04

   Monitoring for Data and Model Drift

   A deployed model degrades quietly as the world drifts away from its training data, and accuracy alone will not warn you in time. This module builds the observability layer: detecting data drift and concept drift, tracking prediction quality when ground truth is delayed, and watching operational health, then setting the alerting thresholds and retraining triggers that close the loop from a live signal back to a safe redeployment.

   2 lessons · 5 quiz questions · assignment