InfoQ Homepage Architecture & Design Content on InfoQ
About this Podcast
Intended for architects and senior developers.
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Getting Value out of an ML Model with Philip Howes
We are talking with Philip Howes about how to get value from your ML model as fast as possible. We will also talk about how to improve your deployed model, and what tools you can use when setting up ML projects. We conclude by discussing how stakeholders should be involved, and what makes up a complete ML team.
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Swyx on Remote Development Environments and the End of Localhost
Shawn Wang (swyx), head of developer experience at Airbyte, and Daniel Bryant discussed the rise of remote development environments. Topics covered included whether remote development experiences are good enough to see the death of local(host) development, what a wishlist might look like for the ultimate developer experience, and how cloud native organizations are currently developing software.
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InfoQ AI, ML and Data Engineering Trends Report 2022
There have been a lot of innovations and developments in the AI and ML space since last year. In this podcast, InfoQ’s AI, ML, and Data Engineering editorial team discusses the latest trends that our readers should find interesting to learn about and apply in their own organizations when these trends become mainstream technologies.
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Principles of Green Software Engineering with Marco Valtas
In this episode, Marco Valtas, technical lead for cleantech and sustainability at ThoughtWorks North America, discusses the Principles of Green Software Engineering. The principles help guide software decisions by considering the environmental impact. The principles are intended for everyone involved in software, and emphasize that sustainability, on its own, is a reason to justify the work.
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Omar Sanseviero on Transformer Models and Democratizing Good ML Practices
Live from the venue of the QCon London Conference we are talking with Omar Sansevier about Hugging Face, the limitations and biases of machine learning models, the carbon emitted when training large scale machine learning models, and democratizing good ML practices.
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