A self-paced flagship bootcamp on the data and compute infrastructure that machine learning depends on — the layer beneath the models. It covers six stages: what the infrastructure has to provide, data foundations and lineage, feature engineering infrastructure, training infrastructure and accelerators, serving and inference, and monitoring, governance and cost.

The premise is that most failed machine learning programmes fail on infrastructure rather than modelling: data nobody can find, features that differ between training and serving, models nobody can reproduce, and inference costs nobody predicted. Tech Skills Library is independent and issues no certification or credential.

Modules

Stage One — What the Infrastructure Has to Provide

Lessons

• Why Programmes Fail Below the Model Preview
• The Platform Layers
• Team Shape and Ownership

Stage Two — Data Foundations

Lessons

• Storage and Access for Training Workloads
• Versioning and Reproducibility
• Labelling and Data Quality

Stage Three — Feature Infrastructure

Lessons

• The Training-Serving Skew Problem
• Feature Store Architecture
• Streaming Features and Freshness

Stage Four — Training Infrastructure

Lessons

• Accelerators and Utilisation
• Distributed Training
• Orchestration Experiments and the Registry

Stage Five — Serving and Inference

Lessons

• Serving Patterns
• Optimising Inference
• Deployment Safety

Stage Six — Monitoring Governance and Cost

Lessons

• Detecting Degradation
• Governance and Documentation
• Cost Attribution and the Career Path