DevOps, DataOps, MLOps

Duke connects software delivery, data operations and machine-learning operations within a pipeline. Model development and deployment sit alongside end-to-end operations and an introduction to Rust for MLOps. It is a continuation for learners who already use development and container tools and want a broader operational view.

What you will learn

• Compare DevOps, DataOps and MLOps responsibilities.
• Build operations pipelines for machine-learning work.
• Connect model development with production deployment.

Who this suits

Learners with intermediate Python, Git, Docker and Kubernetes experience, following the provider’s requirements.

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

Map a fictional model release and identify the checks needed when application code, training data or model artifacts change independently.

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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