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From Generative AI to Agentic Systems: Become Industry-Ready in ML & AI

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What to expect

The AI industry is at a pivotal moment. While generative AI tools like ChatGPT, Claude, and Gemini have revolutionized how we create content, code, and analyze data, the next frontier is already here: agentic AI systems that don't just generate outputs — they plan, reason, use tools, and execute complex multi-step workflows autonomously.

This online masterclass cuts through the hype to give you a clear, technical understanding of what's actually changing in the AI landscape. Hosted by Sandeep Giri, this session is designed for anyone building or planning a career in Machine Learning and AI who wants to understand not just the buzzwords, but the fundamental architectural shifts reshaping the industry.

What You'll Learn

The session covers the critical distinction between prompt-based generative models and structured agentic systems. You'll explore:

  • How Large Language Models (LLMs) function under the hood and their inherent limitations
  • What makes an AI system truly "agentic" — the technical components enabling autonomous decision-making
  • Real-world architectural patterns for building AI agents that can reason through problems and take actions
  • The skills gap between traditional ML engineering and modern AI system development
  • Concrete enterprise use cases where agentic AI is already delivering value
  • What "industry-ready" actually means in 2026's rapidly evolving AI job market

Why This Matters Now

The industry is experiencing three fundamental shifts that will define AI careers over the next few years. First, the move from prompt engineering to AI system architecture — knowing how to chain tools, manage memory, and orchestrate workflows. Second, the evolution from content generation to autonomous execution — building systems that can complete entire tasks, not just respond to queries. Third, the transition from model usage to end-to-end ML deployment — understanding how to ship AI systems that work reliably in production.

If you're working with tools like Cursor, Replit, or Windsurf to build AI-powered applications, understanding these architectural patterns will help you design more capable systems. The session takes a practical, no-fluff approach — focusing on what you actually need to know to stay competitive in ML and AI roles.

This masterclass-style meetup is particularly valuable for developers, data scientists, and ML engineer

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