ML foundations, training systems, deployment serving, and production monitoring.
Most ML models that work in a notebook fail the moment they go to production. MLOps is the platform-engineering specialty that closes the gap — versioning, serving, monitoring, retraining — so models stay accurate as the world changes.
ML basics and data contracts. The infrastructure-perspective primer that exposes why notebooks fail in production — and the minimal ML platform skeleton you build before anything else.
Why ML systems fail in production, the ML lifecycle from an infrastructure perspective, architecture patterns, environments + reproducibility, versioning everything, experiment tracking, and a minimal ML platform skeleton you ship before anything else.
Data contracts for ML pipelines, validation frameworks, feature pipeline testing, handling late + backfilled data, idempotent pipelines, observability, and a production-grade validated feature pipeline as the capstone.
Feature stores and training infrastructure. Where consistent training-serving features meet distributed training and reproducible runs.
Why feature stores exist, point-in-time correctness, building a Feast feature store, streaming ML architecture (Kafka + Flink), real-time feature computation, online-serving latency design, and a hybrid feature platform that unifies batch + streaming.
Training pipeline design, distributed training (Ray + Spark), hyperparameter search infrastructure, model registry patterns, reproducible training runs, and an automated training pipeline that triggers on data freshness.
Deployment, monitoring, and modern MLOps. Where models graduate from a notebook to a self-healing platform that ships both classical ML and LLM applications.
Inference architectures, building a model API, container deployment, Kubernetes fundamentals for ML, serving frameworks (TorchServe / vLLM / BentoML), safe deployment strategies (canary / blue-green / shadow), and deploying a model to a production cluster.
Monitoring stack design, data drift detection, model performance monitoring, alerting strategy, root-cause analysis + debugging, retraining automation, and a self-healing ML system that retrains itself when drift exceeds threshold.
LLMOps architecture, vector databases for ML, RAG data pipelines, evaluation frameworks, cost + scaling for LLMs, governance + security — and the grand capstone: a production ML platform that ships both classical models and LLM applications side-by-side.
Without MLOps infrastructure, you risk:
MLOps (Machine Learning Operations) is the practice of deploying, monitoring, and maintaining ML models in production. It covers the full ML lifecycle — from training pipelines and feature stores to model serving, drift detection, and automated retraining. Used by teams at Google, Uber, and Airbnb to operate thousands of ML models reliably at scale.
Most ML models that work in notebooks fail in production. At Google, MLOps practices ensure models are retrained automatically when data distributions shift. Production MLOps requires deployment infrastructure, monitoring for drift, and automated pipelines that keep models performing as the world changes.
MLOps extends DevOps with ML-specific concerns: model versioning, data drift, feature management, and experiment tracking. DevOps manages code; MLOps manages code, data, and models together.
MLOps focuses on the ML model lifecycle. Data engineering focuses on data pipelines. MLOps builds on data engineering foundations and adds model-specific infrastructure and monitoring.
MLOps is the operational practice of maintaining ML in production. ML engineering includes model development. MLOps focuses on reliability, monitoring, and automation rather than model architecture.
MLOps is the platform-engineering specialty that hires staff-level. This skill proves you can take a model from notebook to production and keep it working — versioning, serving, monitoring, retraining — the role Google, Uber, and Airbnb pay top-of-band to staff their ML platform teams.
MLOps is the practice of deploying, monitoring, and maintaining ML models in production. It covers training pipelines, model serving, drift detection, and automated retraining for reliable AI systems.
MLOps is evolving with LLMOps but remains essential. Every production ML system needs deployment, monitoring, and maintenance. The principles apply whether you are serving traditional ML models or LLM applications.
Core concepts take 2-3 weeks. Building production MLOps with feature stores, serving infrastructure, and monitoring takes 2-3 months of hands-on practice.
Data engineers working on ML teams need MLOps skills. The infrastructure — pipelines, serving, monitoring — is data engineering applied to the ML lifecycle.
MLflow for experiment tracking, Feast for feature stores, Kubernetes for serving, Prometheus for monitoring, and Airflow for pipeline orchestration. Most teams combine multiple tools.
Model drift occurs when a model performance degrades because the real-world data distribution has changed since training. Monitoring detects drift; automated retraining corrects it.