Training

Every MTTL course moves in the same order. You start by framing the problem, then move to practice, pick what is worth measuring, and finish with an action plan for running it at scale. A lesson reads in one sitting. Courses with labs instrument a real stack, and every lab starts with docker compose up; others, such as “Understanding HPC” or the CIO path, need no code.

Your progress

Your progress is stored in this browser and nowhere else. Every multi-lesson course ends with a quiz; programmes still in preparation do not have one yet.

• Understanding generative AI observability

  0 / 10 lessons read

• LLM observability: the labs

  0 / 9 lessons read

• GenAI observability method

  0 / 10 lessons read

• Understanding high performance computing

  0 / 3 lessons read

• HPC AI: observability and security reference architecture

  0 / 4 lessons read

• Governing observability: the CIO path

  0 / 7 lessons read

• VictoriaMetrics as an LLM observability backend

  0 / 8 lessons read

A lesson counts as read once you have scrolled through 80% of it, kept it open for more than 30 seconds, or ticked “Mark as read”. This information stays in your browser.

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AI observability

Observing LLMs, agents and RAG systems in production: answer quality, drift, cost and regulatory evidence. The courses follow a progression: understand (the guide), practise (the labs), master (the method), then go beyond the LLM.

Understanding generative AI observabilityThe reference guide, to read first: LLMs, RAG, agents and MCP; five signals, four system types, six maturity levels, seven steps, evaluators, tools, operations. Practitioner level.

LLM observability: the labsHands-on, after the guide: 5 modules and 4 labs on a sovereign stack for LLMs and RAG, GenAI conventions, drift, continuous evaluation, cost and SLOs. Practitioner level.

GenAI observability methodTo master the subject, once the guide and the labs are done. LLM, agent, RAG and MCP: silent failure, quality SLOs with the Wilson bound, quantified impact with its calculator, regulatory evidence, RACI. Expert level.

Beyond the LLM: GPUs and model qualityGoing further, assuming the LLM course: GPU infrastructure, classic ML, statistical drift detection, retraining. Program published, lessons being written.

HPC and GPUs

Understanding, observing and securing the compute clusters AI is trained on.

Understanding high performance computing3 lessons, no code. What an HPC cluster is, how a job runs on it and why large-scale AI depends on it. Beginner level.

HPC AI: observability and security reference architectureThe five layers of a training cluster, anatomy of a step, the observability stack, eBPF and six attack vectors. Practitioner level.

HPC observability foundationsRedfish exporter, DCGM and Slurm join, MIG contention, a director-readable dashboard. Program published, lessons being written.

Governance and steering

For decision makers: funding, governing, complying and steering observability.

Governing observability: the CIO path7 lessons for decision makers: business case, NIS2 and DORA compliance, RACI and roadmap, FinOps, people and culture, executive dashboard, executive committee toolkit. Practitioner level.

Open source stacks and tools

Getting hands-on with one component of the observability stack, from installation to operations.

VictoriaMetrics as an LLM observability backendThe metrics backend in practice: architecture, installation, MetricsQL, dashboards, alerts, SLOs, operations. Practitioner level.

Where to start

For generative AI, the content follows a progression: discover (AI in cross-section), understand (the guide), practise (the LLM labs), go to production (the playbooks, then VictoriaMetrics for LLMs), master (the GenAI method), go beyond (GPUs and model quality). The profiles below enter it at different points.

You are	Start with	Then
Platform engineer or SRE new to AI	AI in cross-section, then Understanding generative AI observability	The LLM labs, then the deployment playbooks and VictoriaMetrics for LLMs
ML engineer or MLOps	Understanding generative AI observability, then the LLM labs	GenAI method, then Beyond the LLM
Architect	AI in cross-section, then Understanding generative AI observability	Deployment playbooks, VictoriaMetrics for LLMs, GenAI method; HPC AI if you run GPUs
HPC administrator	HPC AI: reference architecture	HPC observability
CIO, leadership, finance	CIO path	Exposure calculator, GenAI method parts V to VII
Team manager	CIO path, lessons 3 and 5	Alert fatigue simulator

Revised on 4 October 2026: “Where to start” table aligned with the progression of the generative AI content (guide before the method, labs, production, beyond the LLM).