ML and AI Data Infrastructure Bootcamp
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Stage One — What the Infrastructure Has to Provide
Why Programmes Fail Below the Model
Preview
The Platform Layers
Team Shape and Ownership
3 lessons
Stage Two — Data Foundations
Storage and Access for Training Workloads
Versioning and Reproducibility
Labelling and Data Quality
3 lessons
Stage Three — Feature Infrastructure
The Training-Serving Skew Problem
Feature Store Architecture
Streaming Features and Freshness
3 lessons
Stage Four — Training Infrastructure
Accelerators and Utilisation
Distributed Training
Orchestration Experiments and the Registry
3 lessons
Stage Five — Serving and Inference
Serving Patterns
Optimising Inference
Deployment Safety
3 lessons
Stage Six — Monitoring Governance and Cost
Detecting Degradation
Governance and Documentation
Cost Attribution and the Career Path
3 lessons
Stage Two — Data Foundations
Labelling and Data Quality
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