Director of Machine Learning Insights [Part 2: SaaS Edition]

Director of Machine Learning Insights [Part 4]

Director of Machine Learning Insights

Director of Machine Learning Insights [Part 3: Finance Edition]

Machine Learning Experts - Sasha Luccioni

Machine Learning Experts - Lewis Tunstall

Machine Learning Experts - Margaret Mitchell

huggingface_hub v1.0: Five Years of Building the Foundation of Open Machine Learning

Last Month’s Machine Learning Lessons Learned

The downside of conference travel The post Last Month’s Machine Learning Lessons Learned appeared first on Towards Data Science .

The Contribution of XAI for the Safe Development and Certification of AI: An Expert-Based Analysis

arXiv:2408. 02379v2 Announce Type: replace-cross Abstract: Developing and certifying safe - or so-called trustworthy - AI has become an increasingly salient issue, especially in light of upcoming regulation such as the EU AI Act.

Supercharged Customer Service with Machine Learning

SeisBench DAS: A machine learning framework for Distributed Acoustic Sensing

SeisBench DAS is an extension of the SeisBench library that standardizes distributed acoustic sensing (DAS) data, metadata, labels, and models for machine learning. It leverages the xdas framework for data ingestion and virtual array handling, and PyTorch for model application, providing an efficient engine to apply deep learning models across diverse DAS formats. The framework aims to bridge the gap between model developers and practitioners, facilitating the adoption of deep learning in DAS research and allowing easy integration of future developments.

Beyond State-of-the-Art: Standardising Environmental Impact Metrics for AI Research

The paper highlights that as Large Language Models grow in capability and prevalence, their environmental footprint is increasing, yet the machine learning community lacks standardized carbon accounting practices. An automated review of 5,285 NeurIPS 2025 papers shows almost no reporting of environmental impact. To address this, the authors propose standardized sustainability metrics for training efficiency, heuristics for estimating inference carbon costs, a software tool called carbonbenchmark for tracking emissions, and the SMAJ framework to encourage prioritizing computational efficiency and environmental accountability over marginal accuracy gains.