Feb 24
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Learn how to effectively monitor and observe Generative AI applications in production! š Deploying an LLM is just the first step. Ensuring it performs consistently, stays within budget, and provides accurate answers requires a robust observability strategy. In this video, we break down the difference between monitoring and observability, the specific metrics you need to track (like Latency, Token Usage, and Hallucinations), and how to detect data drift before it impacts your users. We will cover: ā The "Black Box" problem in AI ā Key Performance Metrics: Latency & Throughput ā Cost Management & Token Counting ā Quality Checks: Catching Hallucinations ā The importance of Human Feedback (RLHF) Whether you are an AI Engineer, DevOps professional, or Product Manager, this guide will help you build reliable and high-quality AI systems. š ļø #GenerativeAI #LLM #DevOps #Observability #MachineLearning #AIProduction #TechEducation #SystemDesign Chapters: 00:00 - Monitoring and Observability: Tracking AI Performance 00:23 - Monitoring vs. Observability 00:52 - The 'Black Box' Challenge 01:20 - Performance Metrics: Speed 01:47 - Resource Metrics: Cost & Usage 02:12 - Quality Metrics: Accuracy & Hallucinations 02:39 - Human Feedback (RLHF) 03:05 - Drift Detection 03:33 - The Observability Stack 03:58 - Key Takeaways 04:24 - Outro š Stay Connected: ā¶ļø YouTube: https://youtube.com/@thecodelucky š± Instagram: https://instagram.com/thecodelucky š Facebook: https://facebook.com/codeluckyfb š Website: https://codelucky.com ā Support us by Liking, Subscribing, and Sharing! š¬ Drop your questions in the comments below š Hit the notification bell to never miss an update #CodeLucky