This LLMOps training gives you the methods to move your language models from prototype to production. Between a demo that impresses and an assistant that handles real load, answers reliably and stays under control, there is a gap few teams cross without incident.
Over two days you cover the stages that matter: LLMOps platform architecture, data preparation, model evaluation, governance and compliance, then deployment and scaling. The final part focuses on observability: which metrics to track, and how to spot quality or cost drift before your users do.
The instructor illustrates each concept with demonstrations drawn from real projects, and the session leaves plenty of room for discussion around your own use cases.
Module 1: Introduction to large language models (LLMs) and LLMOps
Module 2: The core components of an LLMOps platform
Module 3: Data preparation and processing in an LLMOps pipeline
Module 4: Model development, evaluation and management with LLMOps
Module 5: Governance, validation and compliance of LLMOps solutions
Module 6: Inference, deployment and model scaling strategies
Module 7: Monitoring, observability and continuous improvement of LLMOps solutions
Module 8: The future of LLMOps and emerging technologies
LLMOps covers the practices, tools and processes that make it possible to run large language models in production reliably, measurably and under control. Where a prototype only needs to produce a plausible answer, a production system must respond within a fixed time budget, at a predictable cost, with consistent quality and full traceability of what was generated. That gap explains why so many generative AI projects never move past the demo stage.
Swiss organisations deploying internal assistants, document search engines or business agents all run into the same questions: how do you know when answer quality starts to degrade, how do you catch drift before users report it, how do you justify a model-assisted decision to an auditor, how do you keep an inference bill from growing unpredictably. LLMOps provides a structured answer to each of these, and that is exactly what this course addresses.
LLMOps inherits from MLOps, which itself inherits from DevOps, but it introduces constraints of its own. A classic machine learning model produces a deterministic output you compare against ground truth. A language model produces text whose quality depends on context, prompt and phrasing. Evaluation can no longer rest on a single accuracy metric.
Knowing what carries over from your existing practices and what genuinely has to be built saves you from rebuilding an entire pipeline where an adaptation would have done. It is one of the most immediately applicable takeaways of these two days.
Most teams instrument their infrastructure and applications properly, then discover that this instrumentation says nothing about what actually matters to them: the relevance of the answers. An assistant can show perfect uptime and excellent response times while producing steadily less useful output, because a provider changed model version, because the document base evolved, or because a prompt was edited without review.
Observability applied to LLMs therefore means instrumenting the whole chain: the user request, the context retrieval, the model call, the answer produced, the associated cost and any user feedback. It requires specific metrics, reference evaluation sets and alert thresholds defined in advance. The course details this setup and shows how to connect it to an existing monitoring stack, a topic you can explore further with Observability with Prometheus and Grafana (PRM-01).
LLMOps is a young field where the available documentation mixes vendor messaging, standards still being stabilised and contradictory feedback from the field. Sorting what is mature from what is not, on your own, is a substantial investment, with the risk of building a pipeline around a tool that will not exist in eighteen months.
These two days save you that sorting time. The instructor, a practitioner in the field, presents the approaches that have proven themselves, flags those that remain unstable and explains the trade-offs to make depending on the size of your organisation and your regulatory requirements. The format deliberately favours discussion and demonstration over guided exercises: you leave with recommendations that apply to your context, not with a tutorial to replay.
The course runs in Geneva, in Lausanne and as a virtual class, in small groups, so the instructor can address the situations you bring. It sits within a complete artificial intelligence learning path: you can prepare with Understanding Large Language Models (LLM) in Business (AI-01-05) if your teams are new to the subject, or with Developing LLM and RAG Applications with LangChain (AI-06-06) if you already build applications and now want to operate them properly. Temptraining funding may be available depending on your situation.
The logical next step is rarely to deploy a complete platform. It starts with instrumenting a single existing application, defining three to five metrics that genuinely matter to its users, then assembling a reference evaluation set of around fifty representative cases. That modest baseline is already enough to catch the most expensive regressions. Cost control comes next, followed by formalising the governance framework. This step-by-step progression, presented at the end of the session, avoids the tunnel effect of an over-ambitious programme.
Do I need to know how to train a model to attend?
No. LLMOps is about operating existing models, not training them. Operational experience on a project using a language model is enough.
Is the course tied to a specific vendor?
No. The principles presented apply to proprietary models accessed through APIs as well as to open models hosted in-house. The demonstrations cover several environments.
Are there hands-on labs on a dedicated environment?
This session is based on instructor-led demonstrations and discussion around real cases, with no technical environment provided to participants. This format allows more time to be spent on your own challenges.
Does LLMOps also apply to agents and multi-agent systems?
Yes. Agents amplify monitoring challenges, since a single request triggers several chained calls. The observability principles covered in the course apply directly.
How does this differ from an MLOps course?
An MLOps course deals with the lifecycle of models trained in-house. LLMOps focuses on what is specific to language models: prompt management, qualitative evaluation, cost control per token and traceability of generations.
Does this course lead to a certification?
No, there is no recognised LLMOps certification to date. An ITTA achievement badge is awarded at the end of the session.
Monday to Friday
8:30 AM to 6:00 PM
Tel. 058 307 73 00
ITTA
Route des jeunes 35
1227 Carouge, Suisse
Monday to Friday, from 8:30 am to 06:00 pm.
