About

A public, structured roadmap for machine learning and AI interviews.

The mission is simple: give candidates a clear prep system for ML, deep learning, MLOps, GenAI, LLMOps, and ML system design interviews without forcing them into expensive or opaque platforms on day one.

The project is now developed as a public GitHub repository with an explicit CI/CD path: automated lint and build checks on pushes and pull requests, release automation for version tags, and Vercel deployment workflow scaffolding for production delivery. The live app is available at ml-interview-roadmap.vercel.app.

What the MVP includes

• Public homepage and daily plan navigation
• 30, 60, and 90-day tracks
• Ordered pillar pages and topic cards from statistics to ML system design
• Question bank and case-study library
• Curated resources for deeper study
• Public GitHub repo with CI, release, and deployment workflow setup

What comes next

• Saved progress and bookmarking
• Interview readiness scoring per user
• AI-generated study plans from resumes and job descriptions
• Mock interview simulations with scoring and feedback
• Community contribution workflows and peer practice

Open repo

Public by default

The repository is intended to be inspectable, forkable, and easy to extend by contributors who want to improve ML interview prep content, UX, and tooling.

CI/CD

Checks and deploy path

GitHub Actions validates the app on push and pull request. Release tags create GitHub Releases, and the repo includes a Vercel deploy workflow tied to the live production deployment at ml-interview-roadmap.vercel.app.

Content model

Content-first foundation

Case studies are stored in versioned MDX so the platform can scale through clear editorial workflows rather than hidden CMS state.