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About this Podcast
Intended for architects and senior developers.
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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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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.
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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.
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Francesca Lazzeri on Machine Learning for Time Series Forecasting
In the podcast, we speak with Dr. Francesca Lazzeri on machine learning for time series forecasting as the main topic which included automated machine learning and deep learning for time series data forecasting, as well as other emerging trends in machine learning development and operations areas including data science lifecycle.
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AI, ML and Data Engineering InfoQ Trends Report - August 2021
How AI, ML and Data Engineering are evolving 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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