6 modules · Self-paced · Practice included

MLOps and Model Deployment

Package a bounded prediction contract, track artifacts and design monitoring and rollback without deploying a cloud service.

18 published lessons17h 30m suggested study2 free introductory modules

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Every module includes three substantial lessons, downloadable source files, practical exercises, explained solutions and a knowledge check. Modules marked free are available under the current public access rules. Protected self-learning modules are free after registration and email verification. Publication, prerequisites and explicit private-access rules still apply.

01Free module

Deploying Models Through APIs

Serialize a transparent model and validate request/response contracts.

3 lessons · 2h 55m suggested

A schema/versioned JSON model artifact
Request boundary and failure tests
A separate local-serving/deployment checklist

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02Free module

Model Versioning and Experiment Tracking

Record source, environment, artifacts and evaluation as one reproducible experiment.

3 lessons · 2h 55m suggested

A full experiment/provenance record
Partition-aware metric comparisons
A promotion/pause and rollback decision

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03Free · verified account

Reproducible ML Pipelines

Connect validation, train-only fitting, frozen evaluation and packaging.

3 lessons · 2h 55m suggested

A stage and leakage map
Deterministic rerun/duplicate-ID tests
An artifact/evaluation consistency rule

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Define pipeline stages and leakage boundaries · Verified account
Make components deterministic and testable · Verified account
Freeze evaluation before publishing an artifact · Verified account

04Free · verified account

Docker and Deployment Environments

Describe a bounded container build and separate source, output and secret configuration.

3 lessons · 2h 55m suggested

A build/runtime responsibility map
An input/output/secret boundary design
An honest container acceptance and rollback record

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Explain what a container does and does not isolate · Verified account
Separate inputs, outputs and secrets · Verified account
Record environment checks and rollback limits · Verified account

05Free · verified account

Model Monitoring and Data Drift

Separate service health, data change and labelled model quality.

3 lessons · 2h 55m suggested

A monitoring metric contract
An executed shift/mutation illustration
An incident/retraining review policy

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Define operational and analytical monitoring · Verified account
Calculate a transparent shift illustration · Verified account
Design response, retraining and human review · Verified account

06Free · verified account

Production ML Project

Assemble a local serving package, tests and operating design while keeping real deployment separate.

3 lessons · 2h 55m suggested

A tested local model/API evidence pack
A monitoring and rollback runbook
A model card and explicit deployment boundary

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Scope a production-style learning package · Verified account
Test the package and its failure boundaries · Verified account
Prepare monitoring and rollback handover · Verified account

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