Python Essentials for MLOps

Duke develops Python skills around the tasks used in machine-learning operations. Functions and classes connect with testing, data tools and API interactions. It is a programming-focused continuation for learners who want to make their operational scripts more understandable, testable and reusable.

What you will learn

• Organize Python code with functions and classes.
• Write and debug tests with Pytest.
• Use data libraries and APIs within an ML workflow.

Who this suits

Learners with some Python, Git, Docker and Kubernetes experience, following the provider’s preparation guidance.

Before you enroll

Check the cloud-account, software and lab requirements before enrolling. Hosting, storage and GPU resources can incur separate charges. Use permitted sample data, record dependencies and test a rollback or failed pipeline step alongside the successful deployment.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Provider access terms can change; review them before paying.

Put it into practice

Write a small sample-data validation script and add tests for an empty file, a missing field and a valid input.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

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