The MLOps Engineer trek
The full ML lifecycle in production: experiment tracking, feature stores, model serving, drift detection, CI/CD for ML, and LLMOps for large language model systems.
ML lifecycle & fundamentals
Understand the full ML lifecycle before automating it: training, evaluation, deployment, and monitoring — and why each stage needs engineering rigor.
Experiment tracking
MLflow and Weights & Biases for tracking experiments, comparing runs, and never losing a model again.
Data versioning & pipelines
DVC for versioning data and pipelines, ensuring every experiment is reproducible from data to model.
Feature stores
Offline and online feature serving, feature sharing across teams, and avoiding training-serving skew.
Model packaging & serving
Packaging models with MLflow and ONNX, serving with FastAPI and TorchServe, and building low-latency inference endpoints.
CI/CD for ML
Automated training, testing, and deployment pipelines for ML models — triggered by code changes, data changes, or model performance degradation.
Model monitoring & drift detection
Keeping models accurate after they ship: data drift, concept drift, prediction monitoring, and automated retraining triggers.
Orchestration for ML
Airflow, Prefect, and Kubeflow for orchestrating complex ML pipelines with dependencies, retries, and scheduling.
Cloud ML platforms
AWS SageMaker, Google Vertex AI, and Azure ML — managed platforms for training, deploying, and monitoring ML models at scale.
LLMOps
The emerging discipline of operating large language models in production: prompt versioning, LLM evaluation, RAG pipelines, and cost optimization.
ML platform engineering
Building internal ML platforms: self-service tooling, governance, and the infrastructure that makes data science teams 10x more productive.
Capstone — production ML system
Build, deploy, monitor, and iterate on a complete production ML system from data to drift-monitored endpoint.
Trek complete. What's next?
You've walked the full roadmap. Now ship the capstone, write about it, and share the path with the next engineer who needs it.