Learn more about upcoming Events
AI Strategy Training
The AI strategy training upskills employees to support the AI transfor- mation through ownership in implementing an AI strategy. Participants learn how to master AI transformation with comprehensive AI strategy to set stage to realize AI’s potential.
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MLOps Day: Operationalizing the EU AI Act
The EU AI Act's regulations aim to foster trustworthy AI, aligning with best practices for scaling AI in production. While these rules support growth, they present challenges at the organizational, process, and use-case levels. Understanding these challenges and sharing insights are key to compliance and innovation.
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AI Use Case Training
Participants develop AI use cases, gain a sound understanding of AI and learn how they can improve process efficiency, product quality and customer experience through the targeted use of AI.
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EU AI Act Basic Training
Participants gain knowledge about the EU AI Act, understand why AI is regulated in the EU, the resulting impacts on AI projects, and appropriate actions to address them.
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TransferLab: Practical anomaly detection
This workshop introduces unsupervised ML techniques for anomaly detection, their strengths and weaknesses and different application areas. For this workshop, we assume some prior exposure to machine learning and the underlying concepts and basic knowledge of Python, which is required to complete the exercises.
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Roundtable: Deployer obligations under the AI Act
This roundtable will convene experts from the appliedAI network group to discuss the implications of Article 26 of the EU AI Act, which outlines obligations for deployers of high-risk AI systems.
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Improving Yield in Electronics Manufacturing with Federated AI
Join us to explore Siemens' enterprise-wide Federated AI initiative to achieve zero defects. False positives disrupt workflows and waste resources. By reducing these false alarms using data-driven insights, Siemens and Katulu optimise the existing system.
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TransferLab: An introduction to Bayesian methods in ML
This is a 2-day workshop introducing basic Bayesian modelling, using practical examples and probabilistic programming. Learning outcomes include understanding the Bayesian methodology and the foundations of approximate Bayesian inference, as well as learning probabilistic programming in pyro.
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