The discipline of deploying, monitoring, and maintaining machine learning models in production — reliably, repeatably, at scale.
MLOps (Machine Learning Operations) is a set of practices that combines Machine Learning, DevOps, and Data Engineering to deploy and maintain ML systems in production reliably and efficiently.
It bridges the gap between experimental model development and robust production systems — addressing the unique challenges that arise when software systems learn from data and change behavior over time.
Unlike traditional software, ML systems can silently degrade as the world changes around them. MLOps provides the infrastructure, tooling, and culture to detect and respond to this.
Click any stage to explore its components, challenges, and best practices.
The most critical — and most overlooked — stage. Before writing a single line of code, teams must rigorously define what success looks like.
ML CI/CD extends traditional software pipelines with data validation, model testing, and automated deployment gates.
name: ML Training Pipeline
on:
push:
paths: ['src/**', 'data/**', 'configs/**']
jobs:
validate-and-train:
runs-on: ubuntu-latest
steps:
- name: Data Validation
run: python validate_data.py --config configs/schema.yaml
- name: Run Training
run: python train.py --experiment ${{ github.sha }}
- name: Evaluate vs Champion
run: python evaluate.py --challenger ${{ github.sha }}
- name: Deploy if Better
if: steps.evaluate.outputs.is_better == 'true'
run: python deploy.py --strategy canary --traffic 10The three types of drift that silently kill production models.
Google's four-level framework for assessing and evolving your MLOps practice.
Filter by category to explore the tools that power modern ML systems.