VISTA ACADEMY · MACHINE LEARNING

Lesson 15: MLOps and Model Deployment

Learn experiment tracking, versioning, automated pipelines, deployment, monitoring, and reliable model operations.

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What Is MLOps?

MLOps combines machine learning, software engineering, automation, and operations so models can be tested, deployed, monitored, and maintained reliably.

From notebook to dependable service

A model that works in a notebook is only one part of a product. MLOps adds repeatable data preparation, testing, version control, deployment, monitoring, and a plan for updates or rollback.

BUSINESS ACTIVITY

A model performs well in a notebook but fails when another analyst runs it. What should the team improve first?

Create a reproducible, documented workflow with tracked dependencies and inputsKeep the notebook on one person's computerHide the error message

CONCEPT CHECK

What is the main purpose of MLOps?

Make model delivery and maintenance repeatable, reliable, and observableGuarantee every model is perfectly accurateReplace all data scientists with automated scriptsOnly make charts for model results

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Why this matters at work

Agent workflows can connect language models to useful tools, but permissions, validation, privacy, logs, and human review are part of the design—not optional extras.

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