implementing-mlops
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubef
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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-grade):
- Enterprise features (RBAC, audit logs, compliance)
- Integrated production monitoring
- Higher cost than W&B
- Good for regulated industries
Selection Criteria:
- Open-source requirement → MLflow
- Team collaboration critical → Weights & Biases
- Enterprise compliance (RBAC, audits) → Neptune.ai
- Hyperparameter optimization primary → Weights & Biases (Sweeps)