InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Jaxon Repp on HarperDB Distributed Database Platform
In this podcast, Srini Penchikala spoke with Jaxon Repp, head of product at HarperDB, about their distributed database platform, edge persistence, and custom functions.
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AI, ML and Data Engineering InfoQ Trends Report
How AI, ML and Data Engineering evolved in 2021 as seen by the InfoQ editorial team. Topics discussed include deep learning, edge deployment of machine learning algorithms, commercial robot platforms, GPU and CUDA programming, natural language processing and GPT-3, MLOps, and AutoML.
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Joe, Florian and Sebastian on the Indy Autonomous Challenge
The Technical University of Munich has won the Indy Autonomous Challenge. A competition for self-racing vehicles. In this we discussies the the event itself, what makes it challenging, and the approach the TU Munich took. We discuss the importance of simulation, the limits of hardware, how Docker helps crossing this gap, and the role of open source software when taking on such a challenge.
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Meenakshi Kaushik and Neelima Mukiri on Responsible AI and Machine Learning Algorithm Fairness
In the podcast, Meenakshi Kaushik and Neelima Mukiri from the Cisco team speak on responsible AI and machine learning bias and how to address the biases when using ML in our applications.
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Rosaria Silipo on Codeless Deep Learning and Visual Programming
In the podcast, Rosaria Silipo talks about the emerging trends in deep learning, with focus on low code visual programming to help data scientists apply deep learning techniques without having to code the solution from scratch.