Unlocking the Power of AI: Deep Dive into MLOps, Machine Learning, Generative AI and AI/ML Platforms
Welcome to our deep dive into the fascinating world of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Generative AI (GenAI) and AIML Platforms! In this video, we explore how companies are leveraging these technologies to build and continuously improve cutting-edge AI applications using MLOps (Machine Learning Operations) based on robust AI/ML platforms.
What You'll Learn:
🤖 Understanding AI, ML, Deep Learning, and Generative AI: Get a clear explanation of these key concepts and how they interrelate.
🛠️ MLOps Essentials: Discover the importance of MLOps in developing, deploying, and maintaining ML models in production.
🌐 Real-World Use Cases: Learn about practical applications such as Retrieval Augmented Generation (RAG) and how they are implemented in enterprises.
🔄 ML Life Cycle: Understand the stages of the ML life cycle, from idea generation and local development to continuous monitoring and improvement.
🚀 MLOps Capabilities: Explore the essential capabilities needed for effective MLOps, including experimentation environments, data tracking, reproducible pipelines, and model registries.
📈 Business Benefits: Find out how MLOps can accelerate time to market, improve efficiency, and ensure regulatory compliance.
💻 AI/ML Platforms: Understand the importance of a unified AI/ML platform that provides all necessary tools and capabilities for seamless development, deployment, and monitoring of AI solutions.
▬▬▬▬▬▬ T I M E S T A M P S ⏰ ▬▬▬▬▬▬
00:00:00 Intro
00:00:07 What is MLOps?
00:00:34 Romano Roth
00:00:39 AI Everywhere
00:01:02 Artifical Intelligence, Machine Learning, Deep Learning, Generative AI
00:03:46 Introdcution to the ML Use Case: Retrieval Augmented Generation (RAG)
00:05:03 The ML Use Case: Retrieval Augmented Generation (RAG)
00:06:30 From ML Use Case to Production
00:08:56 The ML Life Cycle
00:11:48 MLOps
00:12:46 MLOps vs. LLMops
00:12:56 Mops?
00:13:08 LLMops
00:14:15 DevOps and MLOps and LLMOps
00:15:41 Who is MLOps
00:17:00 MLOps is a Continuous Process
00:22:44 Think: ML Life Cycle
00:24:51 Key Benefits of MLOps
00:27:59 MLOps Capabilities
00:30:31 MLOps Architecture
00:31:41 MLOps Architecture Example
00:33:42 Tools for Development of ML Solutions
00:36:35 Outlook
00:37:50 MLOps Maturity
00:40:08 MLOps Needs The Right Foundation
00:42:50 A Platform Offers AI and ML Capabilities “as a Service” to the Company.
00:45:25 Demo
00:46:01 Platform Plane
00:46:49 Documentation AI Assistant
00:47:41 Container Analysis with AI
00:49:23 Zühlke Reference Finder
00:50:36 Observability
00:51:01 Add Applications
00:52:27 Anaylze Logs with AI
00:53:06 Zühlke ZenAI
00:57:30 Architecture of a Platform
00:59:16 Architecture of ML Use Cases with a Platform
01:02:55 Summary
01:05:25 Final Words
01:05:26 Outro
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