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Technical · Planned

Tai Labs Certified AI Infrastructure Engineer

TAI-AIINF

Operates AI services with CI/CD, evaluation pipelines, observability, scaling and cost control.

Effort
11 weeks
Level
Advanced
Passing
75%
Verify a credential
Tai Labs Certified AI Infrastructure Engineer — Compute topology, inference endpoint, deployment pulse and monitoring graphTAI-AIINFAdvanced certificationAI Infrastructure & MLOps

Competency blueprint

Assessment weighting is published up front — 20% knowledge, 30% labs, 20% portfolio and 30% capstone.

  • Platform

    25%

    Cloud, containers and serving

  • Delivery

    25%

    CI/CD, versioning and rollback

  • Observability

    25%

    Monitoring, evaluation pipelines and latency

  • Economics

    25%

    GPU fundamentals, caching, cost and scaling

Assessment blueprint

Knowledge

20%

Labs

30%

Portfolio

20%

Capstone

30%

75% overall to pass. The capstone must pass independently of every other score. Two retakes per assessment window; capstone may be revised twice within 90 days.

Capstone

Deploy a production-ready AI service with CI/CD, monitoring, evaluations, scaling and cost reporting.

Modules

  • Infrastructure fundamentals
  • Cloud architecture
  • Containers and Docker
  • Model serving and inference APIs
  • GPU fundamentals
  • CI/CD
  • Evaluation pipelines
  • Observability
  • Latency and caching
  • Cost optimisation
  • Versioning and rollback
  • Production scaling