Machine Learning Nordic Data Science and Machine Learning Summit 2023 Standardized Modelling Pipeline for Lending: Scalable and Reusable Automation – Aydin Senaydin, ING Bank November 6, 2023
Artificial Intelligence Nordic Data Science and Machine Learning Summit 2023 Elevate Your Existing Models to Vertex AI – Lef Filippakis & Andrew Wu, Tink November 6, 2023
Artificial Intelligence Nordic Data Science and Machine Learning Summit 2023 Integration Taxes for Generative AI with Open Data Lakehouse Architecture in Data Science – Dylienne Every, Cloudera November 6, 2023
Machine Learning Nordic Data Science and Machine Learning Summit 2023 Stream without Stress: Flexibility and Error-handling in Data Distribution Pipeline – Joanna Nordin, Schibsted November 6, 2023
Artificial Intelligence Nordic Data Science and Machine Learning Summit 2023 Aim at Perfection and Fail – Sofie Perslow, Stylee Intelligence AB November 6, 2023
Artificial Intelligence Nordic Data Science and Machine Learning Summit 2023 Maturing on AI Our Learnings – Alberto Barroso & Anibal Sistac, Tetra Pak November 6, 2023
Artificial Intelligence Nordic Data Science and Machine Learning Summit 2023 Navigate the Generative AI Wave to Get Tangible Value – Mia Ryan, Lendo, Schibsted November 6, 2023
Data Science Nordic Data Science and Machine Learning Summit 2023 On a Mission to Mature Data Science at Telia: Navigate Challenges of Enterprise MLOps Adoption – Joel Larsson, Telia Company November 6, 2023
Data Science Nordic Data Science and Machine Learning Summit 2023 AI Act and Its Implications to Data Science, ML & AI Practices – Fredrik Heintz, Linköping University November 6, 2023
Artificial Intelligence Nordic Data Science and Machine Learning Summit 2023 WARA Media & Language: Build Ecosystem around Generative AI in Sweden – Johanna Björklund, Umeå University, WASP November 6, 2023
