Show Notes
Jeff Huber (@jeffreyhuber, Founder/CEO @trychroma) talks vector databases, data integration, RAG, Embedded models, and AI integration.SHOW: 771CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotwNEW TO CLOUD? CHECK OUT - "CLOUDCAST BASICS"SHOW SPONSORS:Datadog Kubernetes Solution: Maximum Visibility into Container EnvironmentsStart monitoring the health and performance of your container environment with a free 14 day Datadog trial. Listeners of The Cloudcast will also receive a free Datadog T-shirt.CloudZero – Cloud Cost Visibility and SavingsCloudZero provides immediate and ongoing savings with 100% visibility into your total cloud spendSHOW NOTES:Chroma HomepageChroma Reaches Major MilestoneChroma GitHubTopic 1 - Welcome to the show. Tell us a little bit about your background, and what brought you to create Chroma.Topic 2 - Our audience, like many out there, is learning AI as fast as they can. So, let’s start with a few basic concepts. What is a vector database, and why is it important to AI?Topic 3 - Does this only help foundational models? What goes into the integration into a model, and can this be used with any model?Topic 4 - Is RAG (Retrieval-Augmented Generation) possible without a vector database?Topic 5 - What are the trade-offs between fine-tuning a model (adding the data in) vs. RAG (keeping the data external)?Topic 6 - What else is needed to create an “AI Stack”?Topic 7 - Let’s talk about Chroma. First off, there is the OSS project which has been a huge success. Over 3 million downloads and 9.5k Github stars and inclusion into some 10k plus projects. Tell everyone a little bit about Chroma and what makes it different. You recently also announced a major milestone, over one million instances runningFEEDBACK?Email: show at the cloudcast dot netTwitter: @thecloudcastnetFEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @EntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow