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Research#ml📝 BlogAnalyzed: Dec 29, 2025 07:53

ML Platforms for Global Scale at Prosus with Paul van der Boor - #468 [TWIMLcon Sponsor Series]

Published:Mar 29, 2021 20:20
1 min read
Practical AI

Analysis

This article from Practical AI discusses Prosus's use of ML platforms for managing machine learning on a global scale. The focus is on an interview with Paul van der Boor, Senior Director of Data Science at Prosus, about his experience at TWIMLcon. The article highlights the practical application of ML platforms in a real-world business context, offering insights into how companies are tackling the challenges of deploying and managing machine learning models across different regions and scales. The show notes are available at twimlai.com/sponsorseries, providing further details.

Key Takeaways

Reference

“The article doesn't contain a direct quote.”

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Research#Machine Learning📝 BlogAnalyzed: Dec 29, 2025 07:57

Scaling Enterprise ML in 2020: Still Hard! with Sushil Thomas - #429

Published:Nov 19, 2020 21:21
1 min read
Practical AI

Analysis

This article summarizes a podcast episode featuring Sushil Thomas, VP of Engineering for Machine Learning at Cloudera. The discussion centers on the challenges of scaling machine learning (ML) efforts within enterprises. Key topics include the impact of COVID-19 on business decision-making, emerging trends in scaling ML, best practices, hybridizing the engineering and scientific aspects of ML, and organizational models for ML teams. The conversation also touches upon the competition for ML talent with large tech companies. The article provides a concise overview of the podcast's content, highlighting the practical challenges and considerations for organizations adopting and expanding their ML initiatives.
Reference

“The article doesn't contain a direct quote, but summarizes the discussion.”

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Technology#Machine Learning📝 BlogAnalyzed: Dec 29, 2025 08:09

Live from TWIMLcon! Scaling ML in the Traditional Enterprise - #309

Published:Oct 18, 2019 14:58
1 min read
Practical AI

Analysis

This article from Practical AI discusses the integration of machine learning and AI within traditional enterprises. The episode features a panel of experts from Cloudera, Levi Strauss & Co., and Accenture, moderated by a UC Berkeley professor. The focus is on the challenges and opportunities of scaling ML in established companies, suggesting a shift in approach compared to newer, tech-focused businesses. The discussion likely covers topics such as data infrastructure, model deployment, and organizational changes needed for successful AI implementation.
Reference

“The article doesn't contain a direct quote, but the focus is on the experiences of the panelists.”

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Research#llm📝 BlogAnalyzed: Dec 29, 2025 08:14

Learning with Limited Labeled Data with Shioulin Sam - TWiML Talk #255

Published:Apr 22, 2019 22:11
1 min read
Practical AI

Analysis

This article discusses active learning as a method for building applications that require a small amount of labeled data. It features an interview with Shioulin Sam, a Research Engineer at Cloudera Fast Forward Labs, focusing on their recent report, "Learning with Limited Label Data." The conversation likely covers the principles of active learning and its growing relevance in deep learning applications. The article's focus suggests an exploration of techniques to improve model training efficiency when labeled data is scarce, a common challenge in many AI projects. The interview format indicates a practical, accessible approach to explaining the topic.

Key Takeaways

Reference

“The article doesn't contain a direct quote, but the subject is active learning.”

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Research#federated learning📝 BlogAnalyzed: Dec 29, 2025 08:22

Federated ML for Edge Applications with Justin Norman - TWiML Talk #185

Published:Sep 27, 2018 21:40
1 min read
Practical AI

Analysis

This article summarizes a podcast episode featuring Justin Norman, Director of Research and Data Science Services at Cloudera Fast Forward Labs. The discussion focuses on Cloudera's research, including a recent report on Multi-Task Learning and upcoming work on Federated Machine Learning for edge AI applications. The article serves as a brief overview, directing readers to the complete show notes for more detailed information. The core focus is on the application of advanced machine learning techniques, specifically federated learning, in resource-constrained edge computing environments.
Reference

“Specifically, we discuss their recent report on Multi-Task Learning and their upcoming research into Federated Machine Learning for AI at the edge.”

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