#225 The Full Stack Data Scientist with Savin...

AI agents are getting easier to build and connect. Open standards now let them discover each other, pass messages and use shared tools. But connecting agents is not the same as getting them to work toward the same goal, and research on multi-agent systems shows that misalignment ...  Show more

Career goals in data have shifted quickly. Data scientist was long the default target, but AI engineer has appeared from almost nowhere, and companies are moving training toward AI literacy, agents, and prompt engineering. Certifications are becoming a common way to prove these s ...  Show more

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The process of building and deploying machine learning projects requires a staggering number of systems and stakeholders to work in concert. In this episode Yaron Haviv, co-founder of Iguazio, discusses the complexities ...

Sophia Rowland, Senior Product Manager at SAS, discusses her journey from data science to product management at SAS, focusing on the integration of AI and analytics. She explains the concepts of Model Ops and ML Ops, the challenges organizations face in operationaliz ...

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Putting machine learning models into production and keeping them there requires investing in well-managed systems to manage the full lifecycle of data cleaning, training, deployment and monitoring. This requires a repeat ...

We revisit the 2018 Microsoft Build in this episode, focusing on the latest ideas in DevOps. Kyle interviews Cloud Developer Advocates Damien Brady, Paige Bailey, and Donovan Brown to talk about DevOps and data science and databases.

For a data scientist, what does it e ...