Essential concepts and skills for Python.
Before diving into MLOps, ensure you're comfortable with...
Essential concepts and skills for Data Science Tools (NumPy, Pandas, Matplotlib).
Essential concepts and skills for Git.
Essential concepts and skills for SQL.
Essential concepts and skills for Linux/Shell Basics.
Focus on ETL processes and tools like Spark for handling...
Essential concepts and skills for Feature Engineering.
Essential concepts and skills for Model Training (TensorFlow, PyTorch).
Essential concepts and skills for Model Evaluation.
Essential concepts and skills for MLflow.
Kubeflow is for orchestrating ML pipelines on Kubernetes;...
Essential concepts and skills for Apache Airflow.
Essential concepts and skills for TensorFlow Extended (TFX).
Essential concepts and skills for DVC.
Learn containerization for reproducible environments.
Orchestrate containers at scale.
Automate ML workflows with tools like GitHub Actions and...
Essential concepts and skills for Cloud Platforms (AWS SageMaker, GCP Vertex,...
Essential concepts and skills for Model Deployment.
Essential concepts and skills for Model Monitoring.
Essential concepts and skills for Scaling ML Models.
Essential concepts and skills for Security & Ethics.
Essential concepts and skills for Advanced Topics (Federated Learning, Explainable AI).
Frequently Asked Questions
Common questions about this roadmap
DevOps automates software delivery. MLOps extends this to ML models, adding concerns like data versioning, experiment tracking, model retraining, and model monitoring that traditional DevOps doesn't address.
Yes. You don't need to be an ML researcher, but you must understand the ML lifecycle — training, evaluation, deployment, and drift — to build effective MLOps pipelines.
Start with MLflow for experiment tracking — it's open source and widely adopted. Then learn cloud-specific platforms (SageMaker, Vertex AI) based on your target employer.
For production-grade MLOps, yes. Kubernetes is the standard for orchestrating containerized ML workloads at scale. Start with Docker, then progress to Kubernetes.
Data Engineers build pipelines to move and transform data. MLOps Engineers build pipelines to train, deploy, monitor, and retrain ML models. There's overlap, but MLOps is specifically model-centric.