Gesamtlänge aller Episoden: 13 days 12 hours 15 minutes
An interview with Max Halford about the benefits of streaming machine learning for systems that need to learn continuously without being taken offline and how the River library supports building those models.
An interview with Travis Addair about how the Predibase platform lets you start building machine learning applications rapidly without an artificial ceiling on what you can create.
A cross-over episode from The Machine Learning Podcast with the team from Deepchecks, exploring the challenges of testing and validating machine learning applications and their work to make it easier.
An interview with Tuhin Srivastava about how the Baseten platform allows data scientists and ML engineers to build a full stack machine learning powered application by themselves in an afternoon
An interview with Florian Wilhelm about PyScaffold, an extensible toolkit filled with templates that have best practices embedded so that you can skip straight to working on the part of your project that you actually care about.
An interview with Bruno Rocha about how the Dynaconf framework for configuration management in Python applications simplifies the challenge of deploying across environments with security and best practices
An interview with Charles Petzold about the second edition of his book "Code: The Hidden Language Of Computer Hardware and Software" and how an understanding of how hardware works can make you a better software engineer.
An interview with Nicolas Höning about the open source FlexMeasures project for building real-time and adaptable energy management systems
An interview with Jigar Desai about his extensive experience building and scaling engineering teams and useful lessons that you can apply to your own work as an engineering leader.
An interview with Brian Pugh about the Belay project that he created to speed up his work on hardware projects built with MicroPython and the various challenges related to developing for micronctrollers