MLOps Fundamentals
Understand MLOps — the practice of combining ML, DevOps, and Data Engineering to deploy and maintain ML systems reliably in production.
What is MLOps?
MLOps (Machine Learning Operations) is a set of practices that combines Machine Learning, DevOps, and Data Engineering to deploy and maintain ML systems in production reliably and efficiently. While 87% of ML projects never make it to production, MLOps addresses this 'last mile' problem. In traditional software, you deploy code. In ML, you deploy code AND data AND models — all three change independently and all three can break your system. MLOps handles: version control for data and models (not just code), automated training pipelines that retrain when data drifts, model serving infrastructure (APIs, batch processing, edge deployment), monitoring for data drift, model degradation, and concept drift, and reproducibility — any team member can recreate any experiment from 6 months ago. The MLOps maturity levels range from Level 0 (manual everything) to Level 1 (automated training pipeline) to Level 2 (fully automated CI/CD with monitoring and auto-retraining).
MLOps vs DevOps
- DevOps: Code → Build → Test → Deploy → Monitor. Artifacts are binaries/containers. Testing is deterministic (pass/fail)
- MLOps: Data + Code → Train → Validate → Deploy → Monitor → Retrain. Artifacts include models, data, and code. Testing is probabilistic (accuracy thresholds)
- Key Differences: ML systems need data versioning, experiment tracking, model registry, feature stores, and data/model quality monitoring — none of these exist in traditional DevOps
- Data Dependency: ML model quality is directly tied to data quality — bad data = bad model. DevOps doesn't have this dependency
- Continuous Training: Unlike software that's deployed once, ML models need periodic retraining as data distributions change over time (concept drift)
- Team Structure: MLOps requires collaboration between data scientists, ML engineers, data engineers, and platform engineers — each with different tools and expertise
MLOps Tool Landscape
- Experiment Tracking: MLflow, Weights & Biases, Neptune — log hyperparameters, metrics, artifacts, and code for every experiment run
- Model Registry: MLflow Model Registry, Vertex AI Model Registry — version, stage, and approve models for production deployment
- Feature Stores: Feast, Tecton, Hopsworks — centralized feature management ensuring training-serving consistency
- Pipeline Orchestration: Airflow, Kubeflow Pipelines, Prefect, Dagster — automate and schedule ML workflows
- Model Serving: TensorFlow Serving, TorchServe, Triton, BentoML, vLLM — serve models as APIs with batching and scaling
- Monitoring: Evidently AI, WhyLabs, Arize — detect data drift, model degradation, and prediction anomalies
- Platforms: AWS SageMaker, Google Vertex AI, Azure ML, Databricks — end-to-end managed ML platforms