Cloud Machine Learning Engineering and MLOps

Duke introduces the engineering work around a cloud-based machine-learning system. AutoML provides one route into model development while broader topics connect it with operations. It is an orientation for learners who know basic Linux and Python and want to understand how modeling fits into a maintained service.

What you will learn

• Explain cloud machine-learning engineering work.
• Explore AutoML within a model-development workflow.
• Connect emerging ML tools with operational needs.

Who this suits

Learners with beginner-level Linux and Python skills, following the provider’s stated prerequisites.

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

Draw a fictional cloud ML workflow and identify where data preparation, training, deployment and operational review occur.

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.

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