Dev Ops for Data Science

What if you could simply tell a recommendation system what you want instead of relying on likes, dislikes, and watch history? Kyle Polich talks with Fuyuan Lyu about the DPR framework, which combines large language models and traditional recommender systems to give users direct c ...  Show more

How can researchers audit recommendation systems when the algorithms are hidden from view? Hieu Le joins Kyle Polich to discuss Auto-Like, a reinforcement learning framework that systematically explores how platforms like TikTok personalize content feeds. The conversation covers ...  Show more

"DevOps is dead."

Well, not exactly. But the DevOps methodology of "you build it, you run it" has been failing development teams for years.

On this week's episode of Dev Interrupted, we sit down with Kaspar von Grünberg, founder & ...

Data scientists and engineers don’t always play well together. Data scientists will plan out a solution, carefully build models, test them in notebooks, then throw that solution over the wall to engineering. Implementing that solution can take months.Historically, the data scienc ...  Show more

As the race to deliver applications ramps up, the wall between development and operations comes crashing down. When it does, those on both sides learn to work together like never before. But what is DevOps, really? Developer guests, including Microsoft’s Scott Hanselman and Cindy ...  Show more

<span style="font-weight: 400;">On the show this week, we're talking updated DevOps practices for 2022 with hosts</span> <span style="font-weight: 400;">Stephanie Wong</span> <span style="font-weight ...