DevOps & MLOps

Rated 4.5 out of 5

DevOps & MLOps

COURSE OVERVIEW

The DevOps & MLOps course develops a structured foundation across four source-defined units. Learners begin with devops foundations, git and continuous integration, progress to containers, delivery and deployment automation, then move into mlops foundations, experiment tracking and reproducibility, and close with model deployment, monitoring, governance and responsible ai. The sequence is supported by theory sessions and course materials, helping learners connect each unit with a consistent learning rhythm.

The source curriculum links Git and continuous integration with containers and deployment automation, then moves into experiment tracking, reproducibility, model deployment, monitoring, governance and responsible AI. Designed for students, technical learners and early-career professionals, the course provides a focused six-week online pathway that preserves the supplied module order and topic pattern. The emphasis remains on the skills and concepts named in the source curriculum, with no unsupported modules, tools or outcomes added.

WHAT YOU'LL LEARN

Explain DevOps foundations and the role of Git in collaborative delivery.
Describe continuous-integration concepts within a delivery workflow.
Develop understanding of containers and deployment automation.
Explain how delivery practices support repeatable software releases.
Describe MLOps foundations and experiment-tracking concepts.
Explain why reproducibility matters in machine-learning workflows.
Interpret model-deployment and monitoring responsibilities.
Recognize governance and responsible-AI considerations in model operations.

Connect software delivery and machine learning operations with reproducible workflows

CURRICULUM

Unit 1: DevOps Foundations, Git and Continuous Integration

Topics Covered:

This opening module establishes devops foundations, git and continuous integration within the DevOps & MLOps learning pathway. The theory session introduces the source-defined concepts and the course materials support structured review and reinforcement. It creates the foundation for the later modules without adding topics beyond the supplied curriculum.

Theory Sessions
Course Materials

Unit 2: Containers, Delivery and Deployment Automation

Topics Covered

This module develops containers, delivery and deployment automation as the next stage of the DevOps & MLOps pathway. The theory session introduces the source-defined concepts and the course materials support structured review and reinforcement. The unit connects the earlier foundation with the concepts introduced in the modules that follow.

Theory Sessions
Course Materials

Unit 3: MLOps Foundations, Experiment Tracking and Reproducibility

Topics Covered

This module develops mlops foundations, experiment tracking and reproducibility as the next stage of the DevOps & MLOps pathway. The theory session introduces the source-defined concepts and the course materials support structured review and reinforcement. The unit connects the earlier foundation with the concepts introduced in the modules that follow.

Theory Sessions
Course Materials

Unit 4: Model Deployment, Monitoring, Governance and Responsible AI

Topics Covered

The final module focuses on model deployment, monitoring, governance and responsible ai, completing the source-defined DevOps & MLOps sequence. The theory session introduces the source-defined concepts and the course materials support structured review and reinforcement. It brings the four-module pathway to a clear conclusion while preserving the exact source topic structure.