Summary Building machine learning systems and other intelligent applications are a complex undertaking. This often requires retrieving data from a warehouse engine, adding an extra barrier to every workflow. The RelationalAI engine was built as a co-processor for your data warehouse that adds a greater degree of flexibility in the representation and analysis of the underlying information, simplifying the work involved. In this episode CEO Molham Aref explains how RelationalAI is designed, the capabilities that it adds to your data clouds, and how you can start using it to build more sophisticated applications on your data. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Your host is Tobias Macey and today I'm interviewing Molham Aref about RelationalAI and the principles behind it for powering intelligent applications Interview Introduction How did you get involved in machine learning? Can you describe what RelationalAI is and the story behind it? On your site you call your product an "AI Co-processor". Can you explain what you mean by that phrase? What are the primary use cases that you address with the RelationalAI product? What are the types of solutions that teams might build to address those problems in the absence of something like the RelationalAI engine? Can you describe the system design of RelationalAI? How have the design and goals of the platform changed since you first started working on it? For someone who is using RelationalAI to address a business need, what does the onboarding and implementation workflow look like? What is your design philosophy for identifying the balance between automating the implementation of certain categories of application (e.g. NER) vs. providing building blocks and letting teams assemble them on their own? What are the data modeling paradigms that teams should be aware of to make the best use of the RKGS platform and Rel language? What are the aspects of customer education that you find yourself spending the most time on? What are some of the most under-utilized or misunderstood capabilities of the RelationalAI platform that you think deserve more attention? What are the most interesting, innovative, or unexpected ways that you have seen the RelationalAI product used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on RelationalAI? When is RelationalAI the wrong choice? What do you have planned for the future of RelationalAI? Contact Info LinkedIn (https://www.linkedin.com/in/molham/) Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast (https://www.dataengineeringpodcast.com) covers the latest on modern data management. Podcast.__init__ () covers the Python language, its community, and the innovative ways it is being used. Visit the site (https://www.themachinelearningpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com (mailto:hosts@themachinelearningpodcast.com)) with your story. To help other people find the show please leave a review on iTunes (https://podcasts.apple.com/us/podcast/the-machine-learning-podcast/id1626358243) and tell your friends and co-workers. Links RelationalAI (https://relational.ai/) Snowflake (https://www.snowflake.com/en/) AI Winter (https://en.wikipedia.org/wiki/AI_winter) BigQuery (https://cloud.google.com/bigquery) Gradient Descent (https://en.wikipedia.org/wiki/Gradient_descent) B-Tree (https://en.wikipedia.org/wiki/B-tree) Navigational Database (https://en.wikipedia.org/wiki/Navigational_database) Hadoop (https://hadoop.apache.org/) Teradata (https://www.teradata.com/) Worst Case Optimal Join (https://relational.ai/blog/worst-case-optimal-join-algorithms-techniques-results-and-open-problems) Semantic Query Optimization (https://relational.ai/blog/semantic-optimizer) Relational Algebra (https://en.wikipedia.org/wiki/Relational_algebra) HyperGraph (https://en.wikipedia.org/wiki/Hypergraph) Linear Algebra (https://en.wikipedia.org/wiki/Linear_algebra) Vector Database (https://en.wikipedia.org/wiki/Vector_database) Pathway (https://pathway.com/) Data Engineering Podcast Episode (https://www.dataengineeringpodcast.com/pathway-database-that-thinks-episode-334/) Pinecone (https://www.pinecone.io/) Data Engineering Podcast Episode (https://www.dataengineeringpodcast.com/pinecone-vector-database-similarity-search-episode-189/) The intro and outro music is from Hitman's Lovesong feat. Paola Graziano (https://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Tales_Of_A_Dead_Fish/Hitmans_Lovesong/) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/)/CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0/)