implementing-mlops

# 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-source standard):
- Framework-agnostic (PyTorch, TensorFlow, scikit-learn, XGBoost)
- Self-hosted or cloud-agnostic deployment
- Integrated model registry
- Basic UI, adequate for most use cases
- Free, requires infrastructure management

**Weights & Biases** (SaaS, collaboration-focused):
- Advanced visualization and dashboards
- Integrated hyperparameter optimization (Sweeps)
- Excellent team collaboration features
- SaaS pricing scales with usage
- Best-in-class UI

**Neptune.ai** (Enterprise