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Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
10 spots per session maximum — 10 already taken
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30 free minutes with a training advisor — no commitment.
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The Training AI Observability - Operational Excellence and AI Leadership training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Training AI Observability - Operational Excellence and AI Leadership training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Training AI Observability - Operational Excellence and AI Leadership training is carried out through:
Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Global state-of-the-art of AI Observability in 2026: trends, leaders, disruptions. Strategic positioning of AI observability in the enterprise value chain. Benchmark of AI Observability practices from the world's most mature organizations. Strategic diagnosis: 360° assessment of your organization's AI observability maturity. Definition of the AI operational excellence vision and guiding principles.
Advanced architecture: unified observability for classical ML, deep learning, and generative AI. Data observability: monitoring data quality upstream of models. Feature observability: tracking features in production, anomaly detection. Model observability: performance, drift, explainability, fairness in real-time. Workshop: design of your organization's target AI Observability architecture.
Advanced LLM monitoring: hallucinations, toxicity, relevance, factual coherence. Observability for autonomous AI agents: traces, decisions, guardrails, escalation. RAG pipelines in production: retrieval quality, ranking, data freshness. LLM inference cost optimization: token analytics, caching strategies, routing. Demonstrations and hands-on: Langfuse, Helicone, Arize Phoenix, LangSmith.
AI SRE culture: error budgets, toil reduction, automation applied to AI systems. AI incident management processes: playbooks, escalation, crisis communication. Immersive war game: simulation of an AI incident cascade on interconnected systems. Structured post-mortem and blameless retrospective on simulated scenarios. Building the AI SRE practice: team, skills, tools, on-call rotation.
Building an AI Observability Center of Excellence: organization, governance, KPIs. Cost model and business case for the board of directors. Strategy for scaling AI Observability skills across the organization. Regulatory compliance: full traceability of AI systems under AI Act and ISO 42001. Development of the 36-month AI operational excellence plan and final defense.
Target audience
CEOs, CTOs, Chief AI Officers, VP Engineering, VP Data, Head of ML Platform, board members, and C-Level top executives steering the AI operational excellence strategy at the highest level of governance in their organization.
Prerequisites
Proven experience in technical leadership, data leadership, or transformation leadership. In-depth knowledge of MLOps architectures and AI systems in production. Familiarity with SRE practices and the challenges of distributed systems reliability. Prior experience leading large-scale AI initiatives.
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