implementing-mlopslisted

# MLOps Patterns Operationalize machine learning models from experimentation to production deployment and monitoring. ## Purpose Provide strategic guidance for ML engineers and platform teams to build production-grade ML infrastructure. Cover the complete lifecycle: experiment tracking, model registry, feature stores, deployment patterns, pipeline orchestration, and monitoring. ## When to Use This Skill Use this skill when: - Designing MLOps infrastructure for production ML systems - Selecting experiment tracking platforms (MLflow, Weights & Biases, Neptune) - Implementing feature stores for online/offline feature serving - Choosing model serving solutions (Seldon Core, KServe, BentoML, TorchServe) - Building ML pipelines for training, evaluation, and deployment - Setting up model monitoring and drift detection - Establishing model governance and compliance frameworks - Optimizing ML inference costs and performance - Migrating from notebooks to production ML systems - Implementing continuous training and automated retraining ## Core Concepts ### 1. Experiment Tracking Track experiments systematically to ensure reproducibility and collaboration. **Key Components:** - Parameters: Hyperparameters logged for each training run - Metrics: Performance measures tracked over time (accuracy, loss, F1) - Artifacts: Model weights, plots, datasets, configuration files - Metadata: Tags, descriptions, Git commit SHA, environment details **Platform Comparison:** **MLflow** (Open-