Bridging The Gap Between Machine Learning And...

Summary In this episode Yetunde Dada discusses Otto, Astronomer’s AI agent for Airflow, and the broader challenge of making agentic tooling actually useful for data engineers. She explored why generic coding assistants often fall short in data workflows, how Otto adds the missing ...  Show more

Summary In this episode Ragnor Comerford talks about OmniGraph, a lakehouse-native graph storage layer designed around the needs of agentic systems. He explores how graphs are primarily a semantic model for representing the world, rather than just a specialized engine for travers ...  Show more

Machine learning models need updating - what's the reliable way to do it? While in Romania, Richard sat down with Annie Talvasto to talk about her work helping to build DevOps practices around machine learning: Building repeatable processes for data ingestions, cleaning, organ ...

In this episode, I talk with Emmanuel Turlay, Founder & CEO of Sematic, about the pivotal role of AI in Machine Learning infrastructure. Emmanuel shares his expertise in designing and building platforms for Machine Learning pipelines at scale, discussing key components such as da ...  Show more

Preamble This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning. Summary The majority of machine learning projects that you read about or work on are built around batch processes. The model i ...  Show more

Snowflake is using AI to help enterprises transform data into insights and applications. In this episode of NVIDIA’s AI Podcast, host Noah Kravitz and Baris Gultekin, head of AI at Snowflake, discuss how the company’s AI Data Cloud platform enables customers to access and manage ...  Show more