{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/443a398dabd44eb2a30584036340f35e\" frameborder=\"0\" width=\"1670\" height=\"1252\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1252,"width":1670,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1252,"thumbnail_width":1670,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/443a398dabd44eb2a30584036340f35e-00001.gif","duration":271.135,"title":"Using MLflow for Deep Learning: A Comprehensive Demo","description":"In this video, I demonstrate the new features of Moflow and provide an end-to-end demo of using fine-tuning for name entity recognition with a llama27b model in Databricks. I showcase the new Moflow website, including the blog, release page, and updated docs. Throughout the video, I highlight the ease of use and flexibility of the UI, such as searching and grouping charts, comparing metrics, and saving checkpoints. This video aims to provide a comprehensive overview of the new features in Databricks for better deep learning workloads."}