AutomateEdge · Roadmap

AI Learning Roadmaps 2026

Five tracks, each with six deep-dive pages. Every roadmap is free, AI-curated, and calibrated against real 2026 job-posting demand from the AutomateEdge jobs pipeline. Pick the track that matches where you want to end up, not where you are starting from.

Pick a track

AI Generalist →

A broad, balanced foundation across the AI stack — math, classic ML, deep learning, LLMs, and production — so you can follow the field without locking into a narrow specialty in year one.

  • 24 weeks
  • 8h/wk
  • Free

AI Engineer →

The software engineer who ships AI features to production — owning prompt strategy, retrieval pipelines, evals, latency budgets, and the user-visible quality of AI-powered products.

  • 24 weeks
  • 8h/wk
  • Free

ML Engineer →

Build, train, and ship machine learning models that survive contact with production — owning the full arc from dataset construction and distributed training to model serving, monitoring, and continuous retraining.

  • 24 weeks
  • 10h/wk
  • Free

Data Scientist →

Own the full analytical workflow — from formulating the question and designing the experiment to communicating findings and turning model outputs into business decisions.

  • 24 weeks
  • 8h/wk
  • Free

MLOps Engineer →

Build and own the infrastructure that keeps ML models reliable in production — from CI/CD pipelines and model registries to drift detection, GPU orchestration, and multi-model serving at scale.

  • 24 weeks
  • 10h/wk
  • Free

What you get in every track

Six dedicated pages per track, each a deep reference document: Skills (the concrete proficiency ladder), Tools (install path + hours to learn), Projects (10-15 portfolio builds with GitHub rubrics), Certifications (free credentials worth completing), Salary (US + regional comp bands refreshed quarterly), and Career path (entry to principal progression). Every page is source-controlled, versioned, and rebuilds to capture market shifts.

Not sure which track?

The Generalist track is the right default for most people. It covers the full AI stack — classical ML, deep learning, LLMs, and production — so you can specialise later from a position of strength. Pick a specialist track if you already have a specific target role (AI Engineer for LLM-product work, ML Engineer for model training + MLOps, Data Scientist for analysis + experimentation, or MLOps for serving + reliability engineering).

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