Overview of the MLOps Lifecycle
The MLOps lifecycle defines how machine learning systems move from raw data to production-grade AI services. Unlike traditional software, ML systems continuously evolve due to data changes.
Stage 1: Data Collection & Validation
Raw data is collected, cleaned, validated, and versioned. Poor data quality directly impacts model performance.
Stage 2: Model Training
Models are trained using reproducible pipelines. Hyperparameter tuning and experiment tracking are critical.
Stage 3: Model Evaluation
Models are validated using test datasets, performance metrics, and bias checks.
Stage 4: Deployment
The model is containerized and deployed via API or batch processing pipeline.
Stage 5: Monitoring
Production monitoring tracks accuracy, latency, and drift.
Stage 6: Continuous Retraining
When performance drops, automated pipelines retrain the model with new data.
This loop ensures long-term AI reliability in production environments.
