Conquer and Rule Generative AI: GenAI Bootcamp

November 25 – 26, 2024 | Berlin

CONFERENCE STARTS IN:

Dive deep into AI fundamentals and build real-world applications

Equip yourself to drive AI innovation and transformation in your organization

Break down complex AI concepts and turn them into practical solutions

Learn to implement AI-driven strategies and lead your team to success

Day 1: Foundation, Strategic Overview and Building blocks

Introduction & Business Impact
Welcome & participant introductions, Agenda and Roadmap overview, GenAI in the World of AI, Definition, business impact and expectations, From Machine Learning to Generative AI, The Generative AI mindset
Generative AI Building Blocks Part 1
What are Large Language Models (LLMs) and how do they work? How do we use them? Context Windows, Tokens and their importance, Prompt Engineering 101: Personas, history, CoT, x-Shot prompting
Generative AI Building Blocks Part 2
Embeddings and Embedding Models - The heart of Generative AI, Chunking data for efficient processing, Vector Databases: Storing and retrieving AI knowledge
Simple GenAI-Applications Part 1
Simple chat with LangChain: Creating your first Generative AI Chatbot, You look better with Streamlit: Building AI GUIs with Streamlit, Chewing chunks of data: Handling large datasets in Generative AI

Day 2: From simple to advanced integration scenarios

Simple GenAI-Applications Part 2
Brief recap of Day 1 learnings, Overview of Day 2 sessions and objectives, Simple RAG: Implementing a basic retrieval augmented generation system, Tools and function calling: Extending AI capabilities, Agents in Generative AI: Automating processes and decision-making, Framework overview: LangChain, LlamaIndex, Haystack & Semantic Kernel
Complex GenAI-Applications and advanced techniques
Debugging and tracing: Troubleshooting you AI-Apps, Improved RAG: What are HyDE and HyQE? Advances RAG with Multi-Retriever: Building complex AI systems with multiple data sources, Hallucinations, Prompt Injections & Co.: Guarding your application against mishaps
LLMs Deep Dive
Selection criteria: Choosing the right LLM for your needs, Running LLMs in the cloud and On-Premises: Pros, Cons and best practices, Family of models: Understanding the different variants and their use-cases, Working with different models: LLM Routing with OpenRouter
Conclusion and Outlook
Recap of key learnings from the last 2 days, Paradigm-shift? The skillset of a developer in GenAI times, Future trents in generative AI: What to watch for
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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?

Trainers

Sebastian Gingter

Sebastian Gingter looks back on over twenty years of experience as a professional software developer. In the course of his work as a consultant at Thinktecture AG, however, his focus is firmly on the future: on modern (web) technologies, both on the client with Type and JavaScript, on Angular on the server with JavaScript under Node.js or with C# and .NET Core. He has been a fun and passionate explainer since 2008, speaks internationally at conferences and has published specialist articles. You can stalk and contact Sebastian aka @PhoenixHawk on Twitter and find his blog at https://gingter.org.

Marco Frodl

Marco Frodl is Principal Consultant for Generative AI at Thinktecture AG and focuses on generative AI in the field of artificial intelligence. He is fascinated by the vision of being able to communicate with apps and devices in natural language. His consulting focus is the development of AI workflows using LLMs from OpenAI or Mistral as well as community-driven large language models such as Metas Llama2, Mixtral, Intel NeuralChat or DeepSeek Coder. He is a fan of LangChain and LangFuse because they allow complex AI processes to be packaged compactly and traced well.

Elevate your Machine Learning journey by adding the MLcon to your schedule. While the ML Fundamental 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.

Take your generative AI skills to the next level with our intensive 2-day GenAI Bootcamp. Unlike traditional AI courses, this bootcamp goes beyond theory, offering a practical, hands-on experience. You’ll learn to build real-world AI applications, from simple chatbots to complex retrieval-augmented generation systems, using the latest Python-based frameworks like LangChain and Streamlit. With expert guidance, you’ll explore the strategic implications of Generative AI, develop future-proof skills, and gain the confidence to lead AI initiatives.

Dates & Prices

The Bootcamp will be held in English.

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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