Learning Paths
Choose a path that matches your background and goals. Follow it linearly, or dip into the steps that feel most relevant right now.
Map your environments
Review Local, Development, Staging and Production environment patterns and map them to your current workflow.
Go to resourceAdd real CI to one project
Use the Continuous Integration page to introduce linting, typechecks and tests to an existing ML repo.
Go to resourceRefactor one pipeline for maintainability
Apply code best practices to one core pipeline: separate concerns, configuration, logging and error handling.
Go to resourceDefine monitoring and drift signals
Draft what you would monitor for your current model in production: latency, errors, business KPIs and drift.
Harden local ML dev
Use the Local Environment track to make local ML dev reproducible: env management, Docker, shell commands.
Go to resourceExperiment with MCP
Build a tiny MCP server from the Development → MCP page and think about how your org could expose tools and resources.
Go to resourceClarify SLOs and runbooks
From the Production environment mindset, write draft SLOs and a basic incident runbook for one ML service.
Go to resourceRead an MLOps role overview
Use the Blog and Roles sections to understand how companies describe the MLOps role and its expectations.
Go to resourceMap your skills to job descriptions
Pick one or two roles and explicitly mark where you are strong vs. weak across infra, data, CI/CD, and monitoring.
Go to resourceConnect with practitioners and recruiters
Use Practitioners, Recruiters and Community links from the sidebar to have 2–3 conversations with people doing the work.
Go to resource