InfoQ Homepage Machine Learning Content on InfoQ
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Real Time ML Pipelines Using Quix with Tomáš Neubauer
Tomáš Neubauer will talk about Quix Streams, an open-source Python library that simplifies real-time machine learning pipelines. Tomáš will discuss various architecture designs, their pros and cons, and demonstrate a real use case of detecting a cyclist crash using Quix Streams and a TensorFlow model.
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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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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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ML Tools to Accelerate Your Work with Cassie Breviu
Live from the venue of the QCon London Conference, we are talking with Cassie Breviu. She will talk about how she got started with AI, and what machine learning tools can accelerate your work when deploying models on a wide range of devices. We will also talk about GitHub Copilot and how AI can help you be a better programmer.
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The InfoQ Podcast 2021 Year in Review: Hybrid Working, Ethics & Sustainability, and Multi-Cloud
In this special year-end wrap-up podcast Thomas Betts, Wes Reisz, Shane Hastie, Charles Humble, Srini Penchikala, and Daniel Bryant discuss what they have seen in 2021 and speculate a little on what they hope to see in 2022. Topics explored included: hybrid working, the importance of ethics and sustainability within technology, and multi-cloud architectures.