Learning Path Overview

MLOps

Bridge machine learning and operations to deliver reliable, scalable, and governed ML systems in production.

Building a model in a notebook is only the beginning. MLOps is the discipline of operationalizing machine learning — making it reproducible, automatable, observable, and maintainable at scale. This path covers the full ML lifecycle from experiment management through pipeline automation, model deployment, and ongoing governance. Learners will build the skills to treat ML systems with the same engineering discipline as any production software.

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Introduction

What this path is about

Building a model in a notebook is only the beginning. MLOps is the discipline of operationalizing machine learning — making it reproducible, automatable, observable, and maintainable at scale. This path covers the full ML lifecycle from experiment management through pipeline automation, model deployment, and ongoing governance. Learners will build the skills to treat ML systems with the same engineering discipline as any production software.

Goals

What you should be able to do

  • Understand the ML lifecycle and where operational discipline is required at each stage.
  • Implement reproducible experimentation with experiment tracking and data versioning.
  • Build automated ML pipelines for training, evaluation, and deployment.
  • Monitor deployed models and maintain governance standards for data quality and fairness.

Path Structure

What is inside the MLOps path

The path is split into practical stages. Each stage prepares you for the next one, so you do not just memorize concepts, you build real delivery readiness.

01Stage One

MLOps Foundations

Understand the operational challenges of ML systems and establish reproducibility as a core habit.

  • The ML lifecycle: experimentation, training, deployment, and decay
  • Experiment tracking: logging parameters, metrics, and artifacts systematically
  • Data and model versioning for reproducible results
  • Environment management and dependency isolation for ML projects
02Stage Two

Pipeline Automation

Automate the end-to-end ML workflow from data ingestion to model evaluation.

  • Data pipeline design: ingestion, validation, transformation, and feature stores
  • Training pipeline automation and scheduled retraining strategies
  • Model evaluation gates and automated comparison against baseline
  • Pipeline orchestration tools and workflow management principles
03Stage Three

Model Deployment and Serving

Package, containerize, and serve ML models reliably across different deployment patterns.

  • Model packaging formats and serving infrastructure overview
  • REST and gRPC APIs for model inference at scale
  • Deployment strategies: shadow mode, canary, A/B testing, and blue-green
  • Containerization and orchestration for ML workloads
04Stage Four

Monitoring and Governance

Sustain model quality in production through continuous monitoring, data governance, and responsible AI controls.

  • Model performance monitoring: accuracy drift, data drift, and concept drift
  • Data quality monitoring and alerting pipelines
  • Responsible AI in production: fairness auditing, explainability APIs, and bias detection
  • ML governance: lineage tracking, audit trails, and regulatory considerations

Lecture Plan

Planned lessons

These lessons represent the current direction. Detailed modules will be expanded progressively as the curriculum is finalized.

MO02Coming Soon

More lessons coming soon

Next Step

More lessons are on the way

This page gives you a clear roadmap. The detailed lessons will be published in phases as we complete each module.

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