Applied AI: From Fraud Detection and Credit Risk Assessment to Defect Classification and Quality Control in Production – What are the Use & Business Cases of Machine Learning in practice?

Machine Learning Models & Natural Language Processing: Which Use Cases of Neural Networks, Computer Vision, Voice Recognition are applied in practice?

Training, Testing & Validation: How should datasets be structured and processed in order to develop robust Machine Learning models?

From Lab to Business Value: How do companies achieve scalability and productivity of Machine Learning projects?

AI & Machine Learning Security: How can AI & Machine Learning algorithms be secured and threats and vulnerabilities be identified at an early stage?

AI-driven infrastructure & hardware: What are the technical requirements for the systems and how can the optimal composition of the technology stack be achieved?

Software Development, Architectures & Tools: What are the advantages and hurdles in selecting suitable tools?

Customer Journey & Data: How can data integration be optimized across BUs, systems and tools? How do you deal with unstructured and poor quality data in the context of the Customer Journey?

Data Analytics: How to accelerate data analytics processes?

Deployment & IT: How to deploy fast through infrastructure as a code?