MLOps (Machine Learning Operations) is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. It combines principles from DevOps, Data Engineering, and Machine Learning.
The ML Lifecycle
- Data Versioning: Tracking changes in datasets to ensure reproducibility.
- Experiment Tracking: Logging hyperparameters, metrics, and code versions for every training run (e.g., using MLflow or W&B).
- Continuous Integration/Continuous Deployment (CI/CD): Automating the testing and deployment of models.
- Model Serving: Exposing models via APIs (REST, gRPC) for real-time inference.
Monitoring and Drift
Unlike traditional software, ML models can degrade over time even if the code doesn't change. This is due to data drift or concept drift.
Monitoring Essentials
- Data Drift: Changes in the distribution of input data.
- Concept Drift: Changes in the relationship between input features and target labels.
- Performance Monitoring: Tracking latency, throughput, and business metrics (like click-through rate).