21 June 2024
| 09:30 | Registration |
| 10:00 | Welcome |
| 10:15 | ML and AI Introduction - Overview of AI Build and Deployment Lifecycle - Data and Data Processing - Major Model Types: Decision Trees, Linear and Logistic Regression, ANN/DL - Pipelines of Transformations - Tests of ML Artifacts - Monitoring and Live Experimentation |
| 11:00 | Break |
| 11:15 | ML Infrastructure and Operations: Introduction and Investment - Data Infrastructure - Model Infrastructure - Test Infrastructure - (Post Deployment) Experimentation Infrastructure - Build vs Buy Considerations |
| 12:00 | AI Journey for Organizations - Lifecycle of AI Develoment within Companies: From Zero to Excellence - Pillars of AI Offering: Infrastructure / Products, Consulting, Training, Research - Stages of Scaling AI Offerings for an Organization |
| 12:30 | Lunch |
| 13:30 | LLM and Gen AI Introduction, Opportunities - Large Language or Vision Models as Parametrized Memory -- Open Source Weights vs Closed: Considerations on Control -- OpenAI, Llama, Mistral, etc. - What LLMs are good at: Summarization, Few Shot Learning - Problems of LLMs: Halluzinations and where do they stem from - Specializing Large Models to Particular Company Data: -- Retrieval Augmentation -- Fine Tuning -- Use Cases - Trends: From Large Language Models to Modular Small Model Pipelines |
| 14:45 | Break |
| 15:00 | Ethical and Legal Considerations for AI - How and Where Biases Creep In - Robustness in AI solutions - Adjusting your AI Testing Strategy for Increasind Regulatory Oversight - From Test to Control: The Path Ahead - Peeking into the EU AI Act |
| 16:00 | Summary |
| 16:15 | Interactive Part, Q&A |
| 17:00 | Cocktail and Nibbles |
| 18:00 | Open End |
