Dev Ops for Data Science

The next evolution of wearable technology may not require new screens or controllers, but new ways to interact with the devices we already wear. Shimon explores how gesture-sensing earbuds could transform virtual reality and wearable computing through intuitive, user-centered int ...  Show more

How do we design robots and autonomous vehicles that understand the unwritten rules of human behavior? Kyle speaks with Cornell Tech professor Wendy Ju about implicit interaction, "Wizard of Oz" prototyping, and what studying pedestrians, self-driving cars, and even robotic furni ...  Show more

Matt Brems from General Assembly joins us to explain what "data science" actually means these days and how that has changed over time. He also gives us some insight into how people are going about data science education, how AI fits into the data science workflow, and how to diff ...  Show more

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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 ...

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 ...

The role of the data scientist is changing. Some organizations are splitting the role into more narrowly focused jobs, while others are broadening it. The latter approach, known as the Full Stack Data Scientist, is derived from the concept of a full stack software engineer, with ...  Show more