Dev Ops for Data Science

Ekaterina (Kat) Fedorova from MIT EECS joins us to discuss strategic learning in recommender systems—what happens when users collectively coordinate to game recommendation algorithms. Kat's research reveals surprising findings: algorithmic "protest movements" can paradoxically he ...  Show more

Anas Buhayh discusses multi-stakeholder fairness in recommender systems and the S'mores framework—a simulation allowing users to choose between mainstream and niche algorithms. His research shows specialized recommenders improve utility for niche users while raising questions abo ...  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 d ...

Matt Stratton beams aboard the Datanauts starship to share his opinions and experiences with DevOps. Is DevOps a role you can hire for, or a culture you create? If it's the later, how do you get started, what are the impacts, and how do you iterate? The post Datanauts 166: Can Yo ...  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