What is MLOps?

You've trained a model that works great in your notebook. Now what? Getting that model into production—and keeping it working—is where MLOps comes in.

MLOps Defined

MLOps (Machine Learning Operations) is a set of practices that combines ML, DevOps, and Data Engineering to deploy and maintain ML systems in production reliably and efficiently.

Practice	Focus	Key Challenge
DevOps	Software delivery	Code changes, deployment
DataOps	Data pipelines	Data quality, freshness
MLOps	ML systems	Model + Data + Code changes

Why ML Systems Are Different

Traditional software has one source of change: code. ML systems have three:

Traditional Software:
  Code → Application

ML Systems:
  Code + Data + Model → Application

This creates unique challenges:

Challenge	Example
Data drift	User behavior changes, model predictions degrade
Training-serving skew	Features computed differently in training vs production
Reproducibility	"It worked on my machine" but with data and models
Silent failures	Model returns predictions, but they're wrong

MLOps vs DevOps vs LLMOps

Aspect	DevOps	MLOps	LLMOps
Artifact	Application code	Model + data + code	Prompts + LLM config
Testing	Unit/integration tests	Model validation + data tests	Evaluation suites
Versioning	Git for code	Git + DVC for data/models	Prompt versioning
Monitoring	Uptime, latency	Data drift, model accuracy	Quality, cost, safety
Retraining	N/A	Continuous/scheduled	Fine-tuning

The MLOps Ecosystem

┌─────────────────────────────────────────────────────┐
│                    MLOps Stack                       │
├─────────────────────────────────────────────────────┤
│  Versioning     │  DVC, Git LFS, Pachyderm          │
│  Orchestration  │  Kubeflow, Airflow, Prefect       │
│  Feature Store  │  Feast, Tecton, Hopsworks         │
│  Model Registry │  MLflow, W&B, Neptune             │
│  Serving        │  BentoML, Seldon, KServe          │
│  Monitoring     │  Evidently, Arize, WhyLabs        │
└─────────────────────────────────────────────────────┘

Market Context

MLOps has become essential as organizations scale ML:

• Market size: $1.1B (2022) → $21.1B projected (2026)
• Growth rate: ~37% CAGR
• Salary range: MLOps engineers earn $120K-$240K

Key insight: MLOps is not about any single tool—it's about establishing practices that make ML systems reliable, reproducible, and maintainable.

Next, we'll explore the ML lifecycle and how different stages connect. :::