ML Ops What is it?
MLOps (Machine Learning Operations) is a set of practices and tools that unify machine learning (ML) development and operations (Ops) to deploy and maintain ML models reliably and efficiently in production.
MLOps is the process of taking machine learning models from development to production in a way that's automated, repeatable, and scalable.
In a typical ML lifecycle:
A data scientist builds a model in a notebook.
It works great... locally.
But deploying it for real-world use is hardβyou need version control, testing, monitoring, and updates.
MLOps solves that by bringing DevOps principles (automation, CI/CD, testing, monitoring) into the ML world.
Component
Purpose
Version Control
Track code, data, and models (e.g., Git, DVC)
Data Pipelines
Automate data cleaning, feature engineering
Model Training
Automate and track experiments
CI/CD
Automatically test and deploy models
Model Registry
Store and manage model versions (e.g., MLflow, SageMaker)
Monitoring
Watch for model drift, performance, and data issues
Governance
Ensure reproducibility, compliance, and audit trails
MLflow, Kubeflow, TensorFlow Extended (TFX)
Airflow, Prefect (for pipelines)
DVC, Weights & Biases (for tracking experiments)
Docker, Kubernetes (for deployment)
Azure ML, AWS SageMaker, Google Vertex AI (managed MLOps platforms)
Data Ingestion β Data Validation β Model Training β Model Validation β Deployment β Monitoring β Retraining
You're building a fraud detection model:
Without MLOps: You manually clean data, train models in a notebook, and email a .pkl file to the engineering team.
With MLOps: A pipeline auto-fetches fresh data, trains a model, tests it, deploys it via API, monitors performance, and triggers retraining if accuracy drops.