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๐ŸŽฏ The Problem: Your boss says "just add AI to our reports." Your users want to "ask questions in natural language." But you're not a data scientist, and ChatGPT doesn't know your data. Where do you even start?
๐Ÿ’ก This Evening: We'll show you the AI spectrum in Fabric - from traditional ML to conversational analytics - and which approach fits which use case. No PhD required.
โฑ๏ธ Save yourself: The frustration of picking the wrong AI tool. Copilot, AI Functions, and Data Agents solve very different problems.
What you'll learn:
๐Ÿค– ML Models - Train and deploy models in Fabric, and WHY MLflow is the backbone of Fabric ML (experiment tracking, model registry)
๐Ÿงช Experiments - Track runs, compare models, version artifacts, and WHY experiment tracking prevents ML chaos
โœจ Copilot in Fabric - AI-assisted development across all workloads, and WHY Copilot context matters for quality (semantic model awareness)
๐Ÿง  AI Functions - Call LLMs from T-SQL and Spark, and WHY AI Functions democratize GenAI for SQL developers
๐Ÿ’ฌ Data Agents - Conversational analytics with natural language, and WHY agents beat chatbots for data Q&A (they understand your schema)
AI Spectrum: Traditional ML (Train models) โ†’ Experiments (Track) โ†’ AI Functions (Call LLMs) โ†’ Data Agents (Conversational)
Who should attend: Data Scientists exploring Fabric, Analysts curious about AI capabilities, Developers building AI-powered apps
Agenda:

  • 18:30 - Welcome & Networking
  • 18:45 - ML Models & Experiments
  • 19:10 - Copilot Across Fabric
  • 19:25 - AI Functions Demo
  • 19:40 - Data Agents - The Future of BI
  • 19:55 - Q&A and Discussion
  • 20:00 - Networking

์ด๋Ÿฐ ์ด๋ฒคํŠธ๋„ ์ข‹์•„ํ•˜์‹ค ๊ฑฐ์˜ˆ์š”