About this guide
A concise exam companion for MLOps. It covers the machine-learning lifecycle, data versioning and validation, experiment tracking and model registries, reproducible pipelines, packaging, deployment and serving, scaling and cost, monitoring, data and model drift, retraining, CI/CD for machine learning, testing, and governance. Every chapter ends with a worked example carried through step by step, so you can check the reasoning yourself.
Inside the guide
- Machine-learning lifecycle
- Data versioning and validation
- Experiment tracking and model registries
- Reproducible pipelines
- Packaging
- Deployment and serving
- Scaling and cost
- Monitoring
- Data and model drift
- Retraining
- CI/CD for machine learning
- Testing
Questions students ask
What does MLOps cover?
It covers the machine-learning lifecycle, data versioning and validation, experiment tracking and model registries, reproducible pipelines, packaging, deployment and serving, scaling and cost, monitoring, data and model drift, retraining, CI/CD for machine learning, testing, and governance.
Who is MLOps for?
It is written for university students revising MLOps, and for lecturers and librarians choosing a clear, exam-focused reading-list text.
What format does it come in?
Kindle ebook and paperback on Amazon.
Can I read part of it for free?
Full Marks Press offers a free sample chapter at fullmarkspress.com/free, and the full guide is free to read with Kindle Unlimited.