Showing results for "foundation" in Machine Theory & Artificial Intelligence

Factories create massive amounts of data from IoT sensors, MES, SCADA, quality systems, supply chain, maintenance, and others. Yet most organizations can’t turn it into decisions fast enough. The result is reactive firefighting and AI pilots that stall. The Unified Manufacturing Data Architecture (UMDA) is a practical framework built for industry, not adapted from IT. It shows you how to handle real-time streams, integrate legacy systems, enforce security, and scale AI across sites.

Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models.

Ideal for beginners, intermediates, freelancers, and small-business owners who value results now—not someday. No fluff. No recycled blog posts. No get-rich-quick nonsense—just a toolkit you’ll actually use.

Artificial intelligence is everywhere. This book goes beyond merely a definition of artificial intelligence, delving into a discussion of strategies, tools, vendors, and trends to ensure, as a consultant and/or business leader, you are competitive in 2019 and beyond. Artificial Intelligence: Foundations for Business Leaders & Consultants illustrates how to successfully embed artificial intelligence in yours or your client's organization through a simple view of a complex topic to enabling listeners to leverage AI for competitive advantage.

Kenneth is senior director of research and innovation at CoreAVI and chief technology officer at Squint AI. His work focuses on the intersection of artificial intelligence and determinism, enabling neural networks to execute in safety critical systems. Kenneth has co-authored two articles in the scholarly journal Machine Learning with Applications and several white papers for different publications, including Embedded Computing Design. He also holds several patents under CoreAVI’s auspices.