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Tutorials

Tutorials

Hands-on deployment guides for AI frameworks, tools, and pipelines on dedicated GPU servers. Set up PyTorch, TensorFlow, vLLM, and more from scratch — full root access on bare metal.

Tutorials May 2026

Evaluator LLM as Judge

Using a stronger LLM to grade outputs — the technique, the bias, the cost. Production patterns.

Tutorials May 2026

AI Feature Experiment Design

Designing A/B experiments for AI features — metrics, statistical significance, interaction effects. The discipline.

Tutorials May 2026

Explainability via Output Citations

Forcing the LLM to cite sources for each claim — the prompting and structured-output patterns that produce verifiable outputs.

Tutorials May 2026

Attention Mask Optimisation

Sliding window, sparse attention, and mask-based optimisations for long-context LLM serving. The patterns and the trade-offs.

Tutorials May 2026

AI Runtime Tracing with OpenTelemetry

OpenTelemetry instrumentation for AI applications — traces from gateway through embeddings, retrieval, LLM, response.

Tutorials May 2026

AI Canary Rollback Mechanics

When the canary signals problems, the rollback needs to be fast and clean. The mechanics that make rollback reliable.

Tutorials May 2026

AI Soak Testing Pre-Launch

Soak testing for AI services — sustained-load testing that catches memory leaks, thermal issues, KV cache fragmentation.

Tutorials May 2026

AI On-Call Runbook Template

Template runbook for AI on-call — structure, sections, what to include for each incident class.

Tutorials May 2026

Quantisation-Aware Fine-Tuning

QAT (quantisation-aware training) for LLMs — train with simulated low-precision so the deployed quantised model holds quality.

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