Machine Learning Nordic Data Science and Machine Learning Summit 2023 Embed Recycling to Accelerate ML Inferencing – Jo Kristan Bergum, Yahoo November 7, 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
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 Strategies for Monitoring & Retraining Language Models in Production – Laura Skylaki, Thomson Reuters Labs November 6, 2023
Machine Learning Nordic Data Science and Machine Learning Summit 2023 Building a Lakehouse: Leverage Open Table Formats for Unified Stream and Batch Architecture – Micha Kunze, Maersk November 6, 2023
Artificial Intelligence Machine Learning Nordic Data Science and Machine Learning Summit 2023 Panel – How to Prepare for a Fast-Changing DS/ML/AI Landscape – Trends, Challenges and Opportunities November 2, 2023
Artificial Intelligence Machine Learning Opening Keynote: The Secret to AI Innovation – Cassie Kozyrkov, Google November 1, 2023
Artificial Intelligence Data Innovation Summit APAC 2023 Machine Learning Prediction of Impending Failure Using Machine Learning – Rajat Kumar, ST ENGINEERING November 1, 2023
Artificial Intelligence Data Innovation Summit APAC 2023 Data Science Unified Monitoring of Data Science Platforms, Self-service Analytics Platforms & MLOps – Seah Boon Keong, BAT October 31, 2023
