MLOps Tools: MLflow and Hugging Face

Duke concentrates on two practical parts of an ML workflow: tracking model work and packaging models for use. MLflow projects and registration connect with Hugging Face models, datasets and deployment. It is a tool-focused continuation for learners who already understand Python and container-based development.

What you will learn

• Create and register model work with MLflow.
• Use Hugging Face models and datasets.
• Package and automate a model deployment.

Who this suits

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

Register a permitted sample model with its configuration and create a reproducible inference example that another learner can run.

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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