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00:00 Intro to Devops and AI 00:03:23 Building GenAI Apps 00:04:32 Prompt engineering 00:06:53 Models Selection 00:10:52 Data, Rag & Finetuning 00:14:20 API & Agents 00:15:58 Orchestration Framework 00:20:32 Testing GenAI 00:24:15 Operating GenAI Monitoring, Metrics & Observability 00:26:30 Security, PII , caching, Traceability 00:28:05 Capturing Enduser feedback 00:29:40 Business as Usual , rise of the AI engineer 00:31:41 Creating Customer Trust 00:35:55 Pricing , Innovation tax and Business strategy --- Note: there is a longer version of this talk available via - • Operationalizing GenAI - From Chatgpt to P... The reality in many companies: Marketing: we need more Generative AI Engineering: hold my beer In this emerging field of Generative AI , prompt engineering, and Large Language Models (LLMs) , we are bombarded with new ideas, and it's hard to separate mere ideas from reality. These deceivingly simple applications make it look so easy. Engineering, how hard can it be? This talk will walk you through the steps to bring your dreams to production: Patrick will address both business and technical aspects as he provides examples of the journey from taking it from dev to ops with a dash of security. The key takeaways from this talk are: understanding the transformation journey you are on (prompts, models, orchestration frameworks, apis, agents) : the whole lifecycle of delivering genAI apps the tools landscape that is available to look at (development, testing , operational , observabilty) : langchain, lamaindex, llmshield, gptcache, whylabs, traceloop the business considerations to make (cost, privacy , legal, organizational structure) To conclude, we reflect on the implications of these advancements and their significance in the ever-evolving landscape of technology. Can't wait to share and can't wait to learn from your journey too !