Only Until March 5: ✓ Save up to £575  ✓ 10% Team Discount ✓ Parallel Conferences for £250

Only Until March 5: ✓ Save up to £575  ✓ 10% Team Discount ✓ Parallel Conferences for £250

Only Until February 12: ✓ Save up to €595 ✓ 4-Day Special ✓ 10% Team Discount

Only Until February 12: ✓ Save up to €595 ✓ 4-Day Special ✓ 10% Team Discount

Only Until February 12: ✓ Save up to $995 ✓ Free Workshop Day ✓ 3-Day Special 

Only Until February 12: ✓ Save up to $995 ✓ Free Workshop Day ✓ 3-Day Special 

Only Until February 12: ✓ Save up to 715€ ✓ Free AI Product Strategy Day ✓ Access to devmio

Only Until February 12: ✓ Save up to 715€ ✓ Free AI Product Strategy Day ✓ Access to devmio

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Thank you, New York MLcon community ✓ 2026 dates soon ✓ Join the community newsletter for exclusive content

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Thank you, Berlin MLcon community ✓ 2026 dates soon ✓ Join the community newsletter for exclusive content

Unlocking the Power of AI: ML Fundamentals Bootcamp

Holiday Inn Munich City Center | Munich
June 22 – 23, 2025 (Monday – Tuesday) | 9:00–17:00

EARLY BIRD OFFER ENDS IN

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Conquer big data challenges, and embrace MLOps strategies

Build and deploy Decision Trees, KNN, and Neural Networks.

Empower yourself with ML knowledge to drive automation and innovation

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?

Bootcamp Overview

      • AI has an extensive impact on our lives, from music recommendations to autonomous driving. Companies need to leverage advanced AI systems to stay competitive and create innovative customer offerings. But what does this mean, from a technical perspective, for your business?
      • In this bootcamp, we want to introduce software professionals to modern Machine Learning problems, in order to develop in one day the understanding of where the journey could go in their own company. We’ll look at the basics of supervised and unsupervised learning, discuss the flood of data that needs to be managed and its quality; and round it all off with some MLOps topics.
      • The goal is not to train experts in one day, but to make software experts aware of what challenges can arise with ML in future software projects.

Day One

        • 1. Welcome and Introduction
          • Overview of the workshop objectives and goals
          • Participant introductions and expectations

        • 2. Introduction to Machine Learning
          • What is machine learning?
          • Types of machine learning (supervised, unsupervised, reinforcement)
          • Real-world applications of machine learning
          • The machine learning pipeline (data collection, preprocessing, feature engineering, model training, evaluation, deployment)

        • 3. Hands-on: Classical Machine Learning Algorithm
          • Introduction to specific classical algorithms (e.g., KNN, Decision Trees)
          • Overview of classical algorithms and their use-cases
          • Data preparation and exploration
          • Advantages & Disadvantages of such methods

      • 4. Lab session
        • Get your hands dirty based on your experience from the previous session
        • Model training and evaluation
        • Interpretation of results
        • Algorithm optimization techniques
        • Practical considerations and challenge

Day Two

        • 1. Recap of Day 1
          • Brief review of key concepts from the previous day
          • Q&A session

        • 2. Introduction to Neural Networks
          • Neural network architecture (neurons, layers, activation functions)
          • Training process (backpropagation, gradient descent)
          • Overfitting and underfitting
          • Hands-on: Building a simple neural network
          • Using a popular deep learning framework (e.g., TensorFlow, PyTorch)
          • Training the model on the same dataset as the previous day
          • Evaluating the model’s performance

        • 3. Deep Dive into Neural Networks
          • Hyperparameter tuning
          • Regularization techniques
          • Model optimization
          • Hands-on: Experimenting with different neural network architectures

      • 4. Introduction to FastAPI
        • Building a basic API with FastAPI
        • Integrating the trained model into the API
        • Deploying the API (locally or to a platform like Heroku)
        • Testing the API
        • Close out the session and answer any last questions

Trainer

David Schmidig

David is a Senior AI/ML and CUDA Engineer with over ten years of experience building high-performance machine learning and computer vision systems. His work focuses on translating state-of-the-art research into scalable, production-ready applications, including real-time human pose tracking, action recognition pipelines, and large-scale deep learning workflows. David combines strong expertise in C++ and Python with GPU acceleration using CUDA and modern ML frameworks. Since last year, he has further been working in cryptography, where he focuses on accelerating both cryptographic and machine learning workloads on GPUs. David is passionate about making complex ML concepts accessible and practical, with an emphasis on performance, scalability, and clean system design.

Pascal Wyler

Pascal Wyler is a Senior AI Engineer at Netcetera in Zurich, specializing in the intersection of traditional software engineering and modern AI. With Master’s degrees in Applied Data Analytics from Boston University and ZHAW, he builds production-ready applications using Python, LangChain, and cloud infrastructure. Previously, as a Data Scientist at Deloitte in Washington, D.C., Pascal focused on automotive safety, where he developed NLP-driven early warning systems to identify critical safety risks. He also engineered robust MLOps pipelines and led Generative AI initiatives for enterprise clients. Today, his work centers on architecting scalable AI solutions, including intelligent conversational agents and local LLM deployments. Outside of technical innovation, Pascal is an avid photographer and cyclist who enjoys exploring the Swiss Alps.

Stay tuned &
Learn more about MLcon:

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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