AI Agent Bootcamp: LLMs Development with TypeScript

May 15 & 16, 2025 | London

EARLY BIRD ENDS IN:

Level Up with LLMs: Master AI agent development and scalable systems using TypeScript! Perfect for JavaScript and ML developers seeking advanced AI skills and hands-on training

Build Intelligent Agents with Confidence: Discover how to create production-ready AI systems using LLMs, RAG techniques, and advanced memory management

Start Your AI Journey: Learn to craft smart AI agents, integrate external data, and deploy scalable solutions with state-of-the-art LLM technology

Bootcamp Program Day 1

Introduction
Learn the fundamentals of Large Language Models (LLMs), decision-making pipelines, and context management
Hands-On
Set up your environment with TypeScript and Node.js, and practice crafting prompts, debugging, and troubleshooting AI systems
Enhance
Build smarter AI agents with memory, state management, and external data integration for richer, context-aware responses

Bootcamp Program Day 2

Performance
Master evaluations (Evals) and Retrieval-Augmented Generation (RAG) to optimize AI capabilities
Advanced AI Techniques
Learn structured output strategies, long-term memory management, and human-in-the-loop refinement methods
Deploying and Scaling
Explore best practices for deploying production-ready systems and integrating them seamlessly with applications
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Technical Requirements

      • Proficiency in TypeScript and JavaScript: A foundational understanding of TypeScript and JavaScript is essential for hands-on exercises and building AI systems
      • Prepared Development Environment: Bring a laptop with Node.js installed, a modern code editor (such as VS Code or WebStorm), and a fully functional TypeScript setup configured prior to the workshop (like VS Code or WebStorm)
      • Curiosity and Enthusiasm for AI: A genuine interest in learning about LLMs, engaging with hands-on exercises, and exploring cutting-edge AI technologies.

6 Reasons to attend the Bootcamp

1
Future-Proof Your Skills: Stay ahead in the fast-evolving tech landscape by understanding AI's role in software development
2
Hands-On Learning: Dive deep into Machine Learning essentials with practical examples tailored for software professionals
3
Tackle Real-World Challenges: Learn how to manage data quality and scale AI solutions in your projects
4
Discover AI Opportunities: Identify how AI can drive innovation and competitive advantage in your company
5
Expert-Led Insights: Gain valuable knowledge from industry experts and prepare for the AI-driven future.
6
Get the chance to discuss your individual applications for ML with experts

Für wen ist das Camp geeignet?

Who Should Attend?

      • AI Developers and Engineers: Professionals focused on creating intelligent systems powered by LLMs, eager to deepen their expertise in building and scaling AI agents
      • Software Developers: Programmers seeking to transition into AI, learning to expand their skills with LLMs and scalable systems for real-world applications
      • Machine Learning Experts: Practitioners looking to enhance their ML workflows by integrating LLM-powered intelligent agents and cutting-edge AI methodologies
      • Full-Stack and Backend Developers: Engineers interested in enriching applications with advanced AI capabilities, including memory management, RAG, and scalable architectures

Trainer

Nir Kaufman

Web developer. Community enthusiast. Organizer of meetups. International public speaker. Trainer. Author of books. Google developer expert in Angular and web technologies. Front-end Tech Lead at Tikal.

Elevate your Machine Learning journey by adding the MLcon to your schedule. While the AI Agent Bootcamp provides a solid foundation and hands-on experience, the conference offers a unique opportunity to expand your knowledge even further. Explore the latest advancements in ML, learn from industry leaders, and see how cutting-edge tools and strategies complement the skills you’ve developed in the bootcamp. By attending, you’ll gain a comprehensive understanding of ML’s impact across various domains, enabling you to apply these insights to real-world projects and stay ahead in the rapidly evolving tech landscape.

Enhance your ML experience by adding an additional 2-day intensive AI Agent Bootcamp to your ticket—an unparalleled opportunity to deepen your expertise while immersed in learning. Gain practical skills that perfectly complement the insights and strategies shared at the conference. This hands-on camp will solidify your understanding and give you the confidence to implement web architecture solutions immediately. Maximize your conference experience by joining the camp and walk away with actionable knowledge that sets you apart.

Dates & Prices

Was sie mitbringen sollten?

Grundkenntnisse in Python und Jupyter Notebooks, die am optionalen ersten Tag erlernt werden können.

Für wen ist das Camp geeignet?

Das Camp ist ideal für Softwareentwickler:innen und -achitek:innen, die sich für die Erstellung und Integration von ML- Lösungen und GenAI-Services interessieren und offen für neue Technologien und Best Practices im GenAI-Design und -Entwicklung sind.
Tag 1: Einführung in Python für Machine Learning

Für Teilnehmer:innen gedacht, die ihre Grundlagen in Python für ML-Projekte stärken möchten.

  • Python 101: Wichtige Konzepte anhand von Beispielen.
  • Top 10 ML Python Frameworks & Bibliotheken: Theorie & Praxis.
  • Jupyter Notebook: Praktische Erfahrung mit der interaktiven IDE.
  • Hello ML World: Implementierung von ML-Services.

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Tag 2: Einführung in ML & GenAI

Konzentriert sich auf die Grundlagen und Anwendungen von ML und GenAI.

  • ML-Landschaft: Interaktive Einführung und Modellanwendung für verschiedene Usecases.
  • GenAI-Projekte: Architekturelle Bausteine und einfache Anwendungsfälle.
  • Prompt-Engineering: Bedeutung und Implementierung.
  • Modellintegration: Einbindung von proprietären und Open Source LLMs.
  • Semantische Validierung: Guardrails für User-Input und Modell-Output.
  • Enterprise-Integration: GenAI-Lösungen in Unternehmenssoftware einbinden.
  • Unterschiede in GenAI-Projekten: Herausforderungen und Lösungen im Betrieb.

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Tag 3: GenAI im Eigenbau

Geht um die praktische Implementierung und Optimierung von GenAI-Systemen.

  • RAG-Systeme: Einführung und einfache Implementierung.
  • Chunking: Einfluss auf den ermittelten Kontext.
  • Vektordatenbanken: Nutzung zur Kontextfindung.
  • Systemoptimierungen: Anpassung an individuelle Anforderungen.
  • Evaluation und Qualitätssicherung: Möglichkeiten für den produktiven Betrieb.

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