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Growth Paths / AI Engineering 17: Infrastructure & Production
AdvancedSkills

AI Engineering 17: Infrastructure & Production

29 free lessons, then a project of your own.

29 free lessons, then a project of your own. Free lessons, a free self-check, and real work confirmed by 3 people. Part of the Varsity Curriculum: AI Engineering, adapted from AI Engineering from Scratch (MIT License).

Part of: AI Engineering · Proves for: UG

1 required outcomes2 weeksCredential on completion

Path outcomes

How this is verified: 1 is checked automatically, then confirmed by one person; 4 are confirmed by people who saw the work.

SkillsOptional

Build an End-to-End ML Pipeline

Prerequisite (optional here): An end-to-end ML pipeline; this phase adds LLM serving, cost, load and reliability.

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3 milestones

  1. 1Define the prediction problem, select the dataset, and document architecture decisions
  2. 2Build, train, and evaluate the pipeline with proper methodology
  3. 3Package as a reproducible artifact and present for ML practitioner Q&A
Skills

Learn Infrastructure & Production (AI Engineering Phase 17)

Phase 17: 29 lessons.

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2 milestones

  1. 1Do the phase's lessons in public
  2. 2Put an AI service into production

Resources for this path

No affiliate links. No sponsored placements.

The course map this path is adapted from (MIT License).

Every lesson and its code, free.