In this feature article, Daniel D. Gutierrez, insideAInews Editor-in-Chief & Resident Data Scientist, explores why mathematics is so integral to data science and machine learning, with a special focus on the areas most crucial for these disciplines, including the foundation needed to understand generative AI.
Search Results for: machine learning
The AI Revolution: How Machine Learning has Changed the World in Two Years
October 30, 2024 by
In this contributed article, Ashley Marron, CEO of MindGenius, observes that as we approach the second anniversary of the launch of ChatGPT, it’s important to look at the impact AI has had on business technology, radically changing how companies and industries work, in that short time.
IOP Publishing Launches Series of Open Access Journals Dedicated to Machine Learning and AI for the Sciences
July 19, 2024 by
IOP Publishing (IOPP) launched a series of open access journals dedicated to the application and development of machine learning (ML) and artificial intelligence (AI) for the sciences. The new multidisciplinary Machine Learning series will collectively cover applications of ML and AI across the physical sciences, engineering, biomedicine and health, and environmental and earth science.
Deploying Machine Learning Models at Scale: Strategies for Efficient Production
May 17, 2024 by
In this contributed article, freelance writer Ainsley Lawrence briefly explores deploying machine learning models, showing you how to manage multiple models, establish robust monitoring protocols, and efficiently prepare to scale.
The Data Disconnect: A Key Challenge for Machine Learning Deployment
March 25, 2024 by
This article is excerpted from the book, “The AI Playbook: Mastering the Rare Art of Machine Learning Deployment,” by Eric Siegel, Ph.D., with permission from the publisher, MIT Press. It is a product of the author’s work while he held a one-year position as the Bodily Bicentennial Professor in Analytics at the UVA Darden School of Business.
Book Review: A Hands-on Introduction to Machine Learning
January 22, 2024 by
I was pleased to receive a review copy of this new title from Cambridge University Press, “A Hands-on Introduction to Machine Learning.” The hardcover book is very attractive, well-produced and solid! It will weigh down your backpack for sure. As a university instructor myself, I immediately appreciated author and University of Washington professor Chirag Shah’s pedagogical approach.
2023 ML Pulse Report: The Latest Trends and Challenges in Machine Learning
November 9, 2023 by
Our friends over at Sama recently published a comprehensive report on the potential and challenges of AI as reported by Machine Learning professionals.
Video Highlights: Designing Machine Learning Systems — with Chip Huyen
September 15, 2023 by
Chip Huyen, co-founder of Claypot AI and author of O’Reilly’s best-selling “Designing Machine Learning Systems” joins our good friend Jon Krohn, Co-Founder and Chief Data Scientist at the machine learning company Nebula, to share her expertise on designing production-ready machine learning applications, the importance of iteration in real-world deployment, and the critical role of real-time machine learning in various applications.
DDN Storage Solutions Deliver 700% Gains in AI and Machine Learning for Image Segmentation and Natural Language Processing
September 14, 2023 by
DDN®, a leader in artificial intelligence (AI) and multi-cloud data management solutions, announced impressive performance results of its AI storage platform for the inaugural AI storage benchmarks released this week by MLCommons Association. The MLPerfTM Storage v0.5 benchmark results confirm DDN storage solutions as the gold standard for AI and machine learning applications.
NetSPI Debuts ML/AI Penetration Testing, a Holistic Approach to Securing Machine Learning Models and LLM Implementations
August 8, 2023 by
NetSPI, the global leader in offensive security, today debuted its ML/AI Pentesting solution to bring a more holistic and proactive approach to safeguarding machine learning model implementations. The first-of-its-kind solution focuses on two core components: Identifying, analyzing, and remediating vulnerabilities on machine learning systems such as Large Language Models (LLMs) and providing grounded advice and real-world guidance to ensure security is considered from ideation to implementation.
