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The curriculum structure

Every track follows the same structure: 
Foundation (shared, ~7 weeks)
└── Career Track (~20–22 weeks)
    ├── Level 1 — Core Fundamentals (free)
    ├── Level 2 — Applied Skills
    ├── Level 3 — Production Patterns
    ├── Level 4 — Advanced Systems
    └── Level 5 — Capstone
Foundation is completed once — it unlocks your chosen career track automatically. Each level contains multiple modules. Each module contains 3–6 lessons and 1–3 labs. Labs connect to the Living Learning Graph via skill tags.

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The four career tracks

AI Engineer

LLMs, RAG, agents, and production AI systems. For engineers who want to build AI products.

Data Scientist

Statistical modelling, experiments, and ML in production. For analysts ready to go deeper.

ML Engineer

PyTorch, MLOps, and model serving at scale. For engineers focused on training and deployment.

Data Engineer

dbt, Spark, Kafka, and lakehouse architecture. For engineers building the data stack.

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At a glance

TrackLevelsModulesLessonsLabsDuration
Foundation16316~7 weeks
AI Engineer5177417~22 weeks
Data Scientist5146214~20 weeks
ML Engineer5125412~22 weeks
Data Engineer5155015~20 weeks
Total216427164