MLOps: CI/CD for ML

MLOps is DevOps for machine learning. The same git → CI → deploy → monitor cycle — but with models, data, and statistics joined to it. Companies that get this right ship 10x faster. Most don't.

Learning Objectives

After this lesson, you will be able to:

Explain why standard software CI/CD is not enough for ML and what extra automation you need
Set up experiment tracking (using MLflow or W&B) so you can always reproduce any result
Build a model registry workflow that safely promotes models from 'experimental' to 'serving real users'

Prerequisites

Before You Start

Review

Why

Why This Matters

How Netflix updates its recommendation AI every week without breaking anything -- Netflix's MLOps pipeline automatically retrains models on fresh viewing data, runs quality checks (is the new model actually better?), deploys to production servers, and rolls back instantly if anything goes wrong -- all without a single user noticing; this continuous automation is what makes their recommendations feel like they are always getting smarter
Why your hackathon AI project dies the day after the demo -- it lives in a Jupyter notebook with no version control, no experiment tracking, and no deployment pipeline; nobody else can run it, nobody knows which hyperparameters produced the "good" result, and the model cannot be updated without re-running the whole notebook from scratch; MLOps is the infrastructure that turns a one-time demo into a living product
How companies run hundreds of AI experiments simultaneously -- Netflix and Spotify use MLOps to A/B test dozens of model versions at the same time, automatically tracking which version gets more engagement and promoting the winner to 100% of traffic; without this infrastructure, "improving the model" means guessing and hoping
Build this --> Set up a mini CI/CD pipeline for ML: version your dataset and model with git, track experiments with a free tool (MLflow or Weights & Biases), and write a script that automatically retrains, evaluates against a baseline, and only "deploys" the new model if it beats the old one

What	Why	Tool
Hyperparameters	Reproduce the run	MLflow, W&B
Metrics over time	Compare runs	MLflow, W&B
Code version	Link to exact source	Git SHA, DVC
Data version	Know what data was used	DVC, lakeFS
Environment	Reproduce the environment	Docker, conda lock
Model artifacts	Deploy the exact model	MLflow, S3/GCS
System metrics	Diagnose training issues	W&B, GPU monitoring

Version	Stage	Accuracy	Created
Version 1	Archived	0.82	2024-01-15
Version 2	Production	0.87	2024-02-01
Version 3	Staging	0.89	2024-02-15
Version 4	Development	0.91	2024-03-01

Tool	Strengths	When to pick it
MLflow Model Registry	Self-hosted, framework-agnostic, mature	Default for self-hosted stacks
Hugging Face Hub Enterprise	Massive ecosystem, private repos, inference endpoints	If your team already lives on HF
Vertex AI Model Registry	Tight integration with Vertex pipelines + endpoints	All-in on Google Cloud
SageMaker Model Registry	IAM, lineage, deploy hooks, multi-account	All-in on AWS
BentoML / Bento Cloud	Service-oriented packaging (Bentos), built-in serving	Want model + serving config as one artifact
KitOps (ModelKit) / Replicate	OCI-compliant model artifacts, push to any registry	GitOps-first teams
W&B Models	Tight loop with experiment tracking	Already on W&B for tracking

MLOps: CI/CD for ML

After this lesson, you will be able to:

Before You Start

Software Engineering Had the Same Problem -- 20 Years Ago

Your Reflection

Continue Learning

Discussion

#The Three Dimensions of ML

#MLOps Maturity Levels

#Level 0: Manual Process

#Level 1: ML Pipeline Automation

#Level 2: CI/CD for the ML Pipeline

#The ML CI/CD Pipeline, Step by Step

#Step 1: Code Commit

#Step 2: CI -- Lint + Unit Tests

#Step 3: Training Pipeline Runs

#Step 4: Model Evaluation vs Baseline

#Step 5: If Better -- Promote to Staging

#Step 6: A/B Test in Production

#Step 7: Full Rollout

#Experiment Tracking: The Lab Notebook of ML

#Model Registry: Version Control for Models

Modern model registries (2024-2026 landscape)

#GitOps for Models

#Data Versioning: The Missing Piece

Why it matters

Tools

#CI/CD Pipeline Definition

#Key Takeaways

#Quick Check

Deep Dive

Related Lessons

#Exit Ticket