Hey everyone! Thank you so much for watching the first episode of AI-Native Databases with Andy Pavlo! This was an epic one! We began by explaining the "Self-Driving Database" and all the opportunities to optimize DBs with AI and ML at both the low-level, as well as how we query and interact with them. We also discussed new opportunities with DBs + LLMs, such as bringing the data to the model (such as ROME, MEMIT, GRACE), in addition to bringing the model to the data (such as RAG). We also discuss the subjective "opinion" of these models and many more! I hope you enjoy the podcast! As always we are more than happy to answer any questions or discuss any ideas you have about the content in the podcast! This one means a lot to me. Andy Pavlo's CMU DB course was one of the most impactful resources in my personal education, and I love the vision for the future outlined by OtterTune! It was amazing to see Etienne Dilocker featured in the ML for DBs, DBs for ML series at CMU. I am so grateful to Andy for joining the Weaviate Podcast! Links: CMU Database Group on YouTube: https://www.youtube.com/@CMUDatabaseGroup/videos Self-Driving Database Management Systems - Pavlo et al. - https://db.cs.cmu.edu/papers/2017/p42-pavlo-cidr17.pdf Database of Databases: https://dbdb.io/ Generative Feedback Loops: https://weaviate.io/blog/generative-feedback-loops-with-llms Weaviate Gorilla: https://weaviate.io/blog/weaviate-gorilla-part-1 Chapters 0:00 AI-Native Databases 0:58 Welcome Andy 1:58 Bob’s overview of the series 3:20 Self-Driving Databases 8:18 Why isn’t there just 1 Database? 12:46 Collaboration of Models and Databases 20:05 LLM Schema Tuning 23:44 The Opinion of the System 28:20 PyTorchDB - Moving the Data to the Model 33:30 Database APIs 38:15 Learning to operate Databases 42:54 Vector DBs and the DB Hype Cycle 51:38 SQL in Weaviate? 1:07:40 The Future of DBs 1:14:00 Thank you Andy!