#model-serving

Implement MLOps practices for scalable machine learning deployment, monitoring, and lifecycle management. MLOps pipeline stages: 1. Data versioning: DVC (Data Version Control), data lineage tracking, feature store management. 2. Model training: automated retraining, hyperparameter optimization, experiment tracking with MLflow. 3. Model validation: A/B testing, shadow deployments, performance regression testing. 4. Deployment: containerized models (Docker), API serving (FastAPI, Flask), batch prediction jobs. Model serving strategies: 1. REST API: synchronous predictions, load balancing, auto-scaling based on request volume. 2. Batch inference: scheduled jobs, distributed processing with Spark, large dataset processing. 3. Real-time streaming: Kafka integration, low-latency predictions (<100ms), edge deployment. Monitoring and observability: 1. Data drift detection: statistical tests, distribution comparison, feature drift alerts. 2. Model performance: accuracy degradation monitoring, prediction confidence tracking. 3. Infrastructure metrics: CPU/memory usage, request latency, error rates, throughput monitoring. ML infrastructure: 1. Feature stores: centralized feature management, real-time/batch serving, feature lineage. 2. Model registry: versioning, metadata storage, deployment approval workflows. 3. Experiment tracking: hyperparameter logging, metric comparison, reproducible results. CI/CD for ML: 1. Automated testing: unit tests for preprocessing, integration tests for pipelines. 2. Model validation: holdout testing, cross-validation, business metric validation. Tools: Kubeflow for Kubernetes, SageMaker for AWS, Azure ML, Google AI Platform, target deployment time <30 minutes.