MLOps Platforms: Amazon SageMaker and Azure ML

Duke follows a cloud machine-learning workflow from data engineering and analysis into modeling and deployment. The course discusses AWS and Azure technologies within production-oriented work. It suits learners who can already use Python and development tooling and want to compare how cloud services support the lifecycle.

What you will learn

• Apply exploratory data analysis within a cloud workflow.
• Build models using cloud technologies.
• Train and deploy machine-learning solutions.

Who this suits

Learners with intermediate Python, Git, Docker and Kubernetes experience, following the stated preparation.

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

Plan a fictional cloud model release and record the storage, access, deployment and monitoring requirements before creating any paid resources.

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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