What is MLOps?

What is MLOps?

MLOps (Machine Learning Operations) is the set of practices and tools for deploying, monitoring, and maintaining machine learning models in production. It applies DevOps principles to ML — automating the pipeline from data processing through model training, deployment, monitoring, and retraining.

Why It Matters

Most ML models never make it to production — and of those that do, many degrade over time. MLOps bridges the gap between data science experimentation and reliable production systems. As organizations deploy more AI, MLOps becomes essential for managing model lifecycles, ensuring quality, meeting compliance requirements, and scaling AI operations.

How It Works

The ML lifecycle that MLOps manages:

1. Data management — data versioning, quality checks, feature stores
2. Experimentation — experiment tracking, hyperparameter tuning, model comparison
3. Training — automated training pipelines, distributed training, reproducibility
4. Validation — model testing, bias checks, performance benchmarks
5. Deployment — model serving (API endpoints), A/B testing, canary releases
6. Monitoring — performance tracking, data drift detection, latency monitoring
7. Retraining — automated triggers for model updates when performance degrades

Key MLOps tools:

• Experiment tracking — MLflow, Weights & Biases, Neptune
• Pipeline orchestration — Kubeflow, Apache Airflow, Prefect
• Model serving — TensorFlow Serving, Triton Inference Server, vLLM
• Feature stores — Feast, Tecton
• Monitoring — Evidently AI, WhyLabs, Arize
• Platforms — Vertex AI (Google), SageMaker (AWS), Azure ML

LLMOps — MLOps for LLMs:

• Prompt management and versioning
• Evaluation frameworks (human eval, LLM-as-judge)
• Cost optimization and token tracking
• Fine-tuning pipelines
• RAG pipeline management

Key concepts:

• Model drift — model performance degrades as real-world data changes
• Data drift — input data distribution shifts from training data
• Model registry — centralized catalog of model versions
• CI/CD for ML — continuous integration and deployment for model updates

Example

A fintech company uses MLOps to manage their credit scoring model: automated data pipelines pull daily transaction data, monthly retraining pipelines update the model, evaluation checks ensure fairness across demographics, A/B testing compares the new model against the current one, and monitoring dashboards alert the team when prediction accuracy drops below thresholds.