Gain a solid foundation in ML core concepts.

Explore supervised and unsupervised learning, master data management and quality control techniques, and understand the critical role of MLOps in production environments.

ML Basics & Principles

Learn from Industry Leaders about:

  • Supervised vs. Unsupervised Learning: Explore the two paradigms of ML and how they are used to solve different types of problems.
  • Data Management and Quality: Learn best practices for handling ML datasets while ensuring data integrity and quality
  • Real-World Applications: Discover ML applications across industries from recommender systems to fraud detection
  • Challenges and Considerations: Gain insights into key challenges like data bias, explainability, and ethical considerations.

Track Speakers London 2026

Track Speakers San Diego 2026

San Diego's program will go live soon! Until then, please take a look at

New York's 2024 Program!

Track Speakers Munich 2026

Track Speakers New York 2025

Track Speakers Amsterdam 2026

Track Speakers Berlin 2025

Track Program London 2026

Track Highlights San Diego 2026

Track Program Munich 2026

Track Program New York 2025

Track Program Berlin 2025

Track Program Amasterdam 2026

Track Sessions MLcon Berlin 2026

Track Sessions MLcon Berlin 2026

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Track Sessions Amsterdam 2026

Track Sessions Amsterdam 2026

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Track Sessions San Diego 2026

Track Sessions San Diego 2026

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Track Sessions London 2026

Track Sessions London 2026

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Track Sessions MLcon Munich 2026

Track Sessions MLcon Munich 2026

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Track Sessions MLCon New York

Track Sessions MLCon New York

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Behind the Tracks

AI Agents & Agentic Workflows
Build reliable AI agents and autonomous workflows for enterprise production

MLOps & Open Source LLMs
Deploy, and operate AI models securely—from fine-tuning to production monitoring

Multimodal AI
Build AI systems that process images, audio, and video—beyond language models

AI Strategy, Organization & Governance
Scale AI responsibly—align regulation, human accountability, and organizational readiness

Advanced RAG
Master retrieval-augmented generation—from vector search to production-grade knowledge systems