MLOps Zoomcamp: Free Course

About this course

Move beyond training a model and study the work needed to operate it. This free DataTalks.Club MLOps Zoomcamp contains all 43 videos in the published curriculum, including environment alternatives, practical examples, monitoring, testing and delivery material. It is a technical course for learners with existing programming and machine learning foundations.

Course at a glance

What you’ll learn

• Connect experiment tracking, model management and a registry with reproducible machine learning work.
• Understand pipeline and deployment approaches presented for web, streaming and batch use cases.
• Use monitoring and data-quality concepts to reason about the behaviour of an operating model.
• Relate testing, code quality, Terraform and CI/CD to a maintainable model-delivery workflow.

Skills you’ll gain

• Experiment tracking
• MLflow
• Model deployment
• Data-quality monitoring
• Automated testing
• MLOps delivery

Course Overview

The opening section introduces the course, its environment and a prediction-model example. Experiment tracking and model management then lead into pipelines and deployment strategies. Later lessons examine monitoring, data quality, testing, code quality and infrastructure as code, ending with continuous integration and deployment workflows. Alternative environment-preparation recordings and optional demonstrations remain available. The course’s value is the connection between model development and repeatable operations: an experiment needs a record, a deployment needs an environment, and a running model needs evidence that its behaviour is still appropriate.

How to study the course

• Use the early environment and modelling lessons to establish a working example. Retain both preparation options and choose the one suitable for your practice setup.
• Follow tracking, management and deployment as connected stages. Keep enough information to identify which model and environment produced a result.
• Finish with monitoring and best practices. Review the tests and delivery controls that help you detect problems before and after a change.

Use the searchable classroom lesson list to revisit a specific explanation. Pause after a useful demonstration, reproduce a small example and explain the result in your own words. Keep a short note of what worked, what changed and what you still need to understand before moving on.

Who is this for?

This course suits machine learning engineers, data scientists and software engineers who already understand model development and want production-oriented skills. It is not a first introduction to programming or machine learning.

Before you start

Python, Docker, command-line experience and basic machine learning knowledge are recommended. Hands-on cloud exercises may require separate paid resources. Watching the course at your own pace does not include cohort grading or a certificate.

Put the lessons into practice

Use a small prediction model in a practice repository. Record an experiment, save the model and explain how a deployment selects its version. Add a simple service or batch interface, then define one data-quality check and one behaviour you want to observe after a run. Write a unit test for preprocessing and an integration check for the overall workflow. Keep the environment reproducible and document how a change would be reviewed, tested and delivered. The aim is an explainable operating process, not a claim of production readiness.

This optional activity is provided by WikiFree for independent practice. It is not a graded assignment from the instructor. Keep your working notes, check the result against the task you defined and return to the relevant lesson when an assumption or technique is unclear.

Certificate / Assessment

Watching the public videos and using WikiFree progress tracking does not award a certificate, exam voucher, academic credit or instructor assessment. The provider has separate cohort assessment criteria; those are not included in this self-paced video listing.

Use your practice output to review what you can now explain and repeat independently. Completion tracking records your study progress; it does not replace an assessed qualification or professional experience.

Frequently asked questions

Provider: DataTalks.Club. Videos play from the DataTalksClub channel on YouTube.