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