🌟 Just crossed 1000 followers for The MLOps Podcast on Spotify! 🌟 I thought this is a good opportunity to say thanks + share a teaser for the upcoming episode (launching Thursday)!

🚨 For all professionals in the healthcare industry! 🚑 Check out the latest #TheMLOpsPodcast episode on AI in medical devices with Mila Orlovsky. Learn about collecting data to build edge case datasets, AI that meets regulatory standards, and much more dagshub.com/blog/ai-in-med…

🎙️Excited to launch our newest #MLOpsPodcast episode with the amazing Han! We're getting into the nitty-gritty of NLP and LLMs, making sense of where AI is headed. 🌟 Check it out: dagshub.com/blog/realistic…

🚀 AnimateDiff-Lightning is a lightning-fast text-to-video generation model. 🥳 Thanks to Shanchuan Lin ❤ Xiao Yang ❤ AnimateDiff-Lightning weights are now available on 🐶 DagsHub 📦model: dagshub.com/ByteDance/Anim… 📄paper: arxiv.org/abs/2403.12706

🚀 New Podcast Episode Alert! 🚀 1/7 🎙️ Excited to share my latest episode featuring Idan Gazit, Senior Director of Research at GitHub Next!

I'm heading to the 🇺🇸 US in June and I'd love to meet you if you're into AI and MLOps like me! I'll be in San Francisco from June 9-16 for the Databricks Data+AI Summit and then in Seattle from June 18-21 for #CVPR2025.

🚀 Excited to share my latest article for DagsHub : "Best Data Annotation Tools for Machine Learning That You Need to Know"! 📝 Check out the full article here: dagshub.com/blog/best-data… #MachineLearning #DataScience #AI #DataAnnotation #DagsHub #Tech #Innovation

I’m really proud to share our amazing partnership results with MACSO. Their ambitious ML team led by Hwan is doing mind-blowing work at the intersection of AI, edge computing, AgTech, and more.

How can ML revolutionize ER diagnostics? I'm chatting with Ljubomir Buturovic, VP of ML at Inflammatix, about using ML to diagnose ER infections. • Robust diagnostic/prognostic models • Data heterogeneity challenges • Edge device ML deployment dagshub.com/blog/ml-in-the…

We've enhanced our experiment tracking to let you see your model's predictions and outputs as they evolve during training Visual insight into model behavior is critical, yet overlooked in ML workflows. We're introducing an integrated experiment artifacts view on DagsHub

Active learning and auto-labeling automate improving data quality. Using models to annotate data, minimizes the need for manual labeling and enables teams to focus on reviewing only lower-quality samples.