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SUMMARY:Operationalizing AI: MLOps x LLMOps
DESCRIPTION:About the webinar\nIn this MLOps and LLMOps webinar\, we’ll walk through the entire AI lifecycle – from idea and experimentation to production\, deployment and continuous monitoring\, highlighting how AI differs from traditional software (data-driven\, non-linear\, and sometimes unpredictable even when “done right”). You’ll learn the main deployment patterns (batch/offline\, real-time/online\, and common patterns employed in cloud solutions) and the key trade-offs around latency\, scaling\, and operational reliability. \nWe’ll then connect MLOps and LLMOps in a practical way: versioning data/models/prompts\, reproducibility\, CI/CD\, and testing strategies for probabilistic systems. It’s aimed at data scientists\, ML engineers\, software engineers\, and AI engineers who want a clear\, production-focused view of how to run ML and LLM solutions end-to-end. \nWho is the webinar for? \nIt’s aimed at data scientists\, ML engineers\, software engineers\, and AI engineers who want a clear\, production-focused view of how to run ML and LLM solutions end-to-end. Also suited to those with no experience in building and deploying AI models\, and are curious on AI/ML/LLM Ops. \nKey takeaways for participants: \n\n\nKey differences between AI and traditional software \n\n\nHow these differences translate to model deployment \n\n\nWhat is ML and LLM Ops and how they differ \n\n\nDifferent model deployment strategies \n\n\nSpeaker bio:\nMurilo Kuniyoshi Suzart Cunha (https://www.linkedin.com/in/murilo-cunha/) \nMurilo is a machine learning engineer specializing in productionizing models and applying AI Ops best practices\, with a focus on the evolving landscape of LLMOps. He takes a pragmatic approach to machine learning\, ensuring AI initiatives deliver tangible ROI. An experienced international conference speaker and open source supporter\, Murilo is also the host of the Monkey Patching Podcast.
URL:https://mimer-ai.eu/event/operationalizing-ai-mlops-x-llmops/
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