A practical roadmap for deploying and operating machine learning systems in production — covering version control for ML artifacts, CI/CD pipelines, cloud infrastructure, containerization, orchestration, and model monitoring. Every section is a tool or skill you will actively use on the job.
MLOps is about operationalizing machine learning — not learning it. You need ML knowledge and programming skills before the MLOps-specific sections are meaningful.
A reference to follow when implementing a real network project.
MLOps (Machine Learning Operations) is the engineering discipline that operationalizes machine learning models into reliable, observable, and scalable production services. MLOps engineers bridge the gap between experimental data science prototypes and resilient cloud infrastructure.
This roadmap covers the complete operational ML lifecycle: packaging inference services with FastAPI and Docker, orchestrating reproducible training DAGs with Kubeflow, managing feature stores, deploying canary releases, scaling GPU clusters with Triton and vLLM, and monitoring data drift in real time.
Containerize ML models with Docker, build high-performance FastAPI inference endpoints, version model artifacts with MLflow, and set up basic CI testing.
Key competencies:
Automate training pipelines (Kubeflow / SageMaker), manage feature stores (Feast), implement canary rollouts, and monitor data drift with Evidently AI.
Key competencies:
Scale distributed GPU inference clusters (Triton / vLLM), automate retraining triggers, manage enterprise model governance, and optimize GPU FinOps.
Key competencies:
Data Drift (covariate shift) occurs when the distribution of input data changes over time while the underlying relationship remains the same. Concept Drift occurs when the statistical relationship between input features and target labels changes (e.g., consumer purchasing behavior shifting during economic changes).
A Feature Store provides a centralized repository where feature transformations are computed once and shared across both offline model training (historical batch data) and online inference (low-latency key-value lookups), preventing train-serve skew.
Production ML deployments typically use Canary Deployments (routing 5% of traffic to the new model while monitoring latency and error metrics) or Shadow Deployments (sending production traffic to the new model in the background without returning its output to users) to validate stability safely.
A standard production stack includes MLflow/W&B for experiment and model registry, DVC for dataset versioning, Docker and Kubernetes for container orchestration, Kubeflow/Airflow for pipeline orchestration, Triton or vLLM for high-throughput serving, and Evidently AI with Prometheus for drift monitoring.