Machine Learning Nordic Data Science and Machine Learning Summit 2023 Embed Recycling to Accelerate ML Inferencing – Jo Kristan Bergum, Yahoo byJanaNovember 7, 2023
Machine Learning Nordic Data Science and Machine Learning Summit 2023 Talk the Talk, Walk the Walk: NLP Journey of a Major Swedish Bank – Manne Fagerlind, SEB byJanaNovember 7, 2023
Machine Learning Nordic Data Science and Machine Learning Summit 2023 Use Generative Large Language Models to Create Synthetic Training Data for Sentiment Analysis – Fredrik Olsson, Gavagai byJanaNovember 6, 2023
Machine Learning Nordic Data Science and Machine Learning Summit 2023 Journey towards Using LLMs on Enterprise Digitalization – Weiqing Zhang, Telenor Research, Telenor Group byJanaNovember 6, 2023
Machine Learning Nordic Data Science and Machine Learning Summit 2023 How We Build a Data Lakehouse to Manage PB’s of Data – Joachim Zetterman, Scania byJanaNovember 6, 2023
Machine Learning Nordic Data Science and Machine Learning Summit 2023 Standardized Modelling Pipeline for Lending: Scalable and Reusable Automation – Aydin Senaydin, ING Bank byJanaNovember 6, 2023
Artificial Intelligence Nordic Data Science and Machine Learning Summit 2023 Elevate Your Existing Models to Vertex AI – Lef Filippakis & Andrew Wu, Tink byJanaNovember 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 byJanaNovember 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 byJanaNovember 6, 2023
Artificial Intelligence Nordic Data Science and Machine Learning Summit 2023 Aim at Perfection and Fail – Sofie Perslow, Stylee Intelligence AB byJanaNovember 6, 2023
