Subject archive for "data-science," page 2

This blog post explores the challenges of fine-tuning large language models (LLMs) and introduces resource-optimized and parameter-efficient techniques such as quantization, LoRA, and Zero Redundancy Optimization (ZeRO). By fine-tuning Falcon-7b, Falcon-40b, and GPTJ-6b, we demonstrate how these techniques offer improved performance, cost-effectiveness, and resource optimization in LLM fine-tuning. The blog post also discusses the future of fine-tuning and its potential for unlocking new possibilities in enterprise AI applications.

With the ongoing generative AI hype, one concept is becoming increasingly clear: giant, generic generative AI models, by themselves, are not the key to unlocking business value. While they are excellent for experimentation, entertainment, and some limited end-user work augmentation (ChatGPT might have helped with parts of this blog), they often fall short in terms of performance, accuracy, and risk when they aren't production grade.

Generative AI is moving swiftly from intriguing novelty to top priority for your digital transformation strategy. It is hijacking conversations everywhere, from the boardroom to the dining room. But what do the top data science leaders and practitioners from the world’s most advanced AI companies really think? How transformative do they believe Generative AI really is? What problems and risks do they see, and how are they going about turning Generative AI into tangible business value? We surveyed them to find out.

My name’s Jack Parmer, and I’m the former CEO and co-founder of Plotly.

It's becoming clear that the traditional “citizen data scientist” approach, focusing on no-code tools, has become an evolutionary dead end. Organizations who have pursued this route have little to show beyond PoCs and one-off successes despite years of investment in training and underutilized, proprietary tools. The best that can be said is that these efforts have been a costly way of democratizing data prep and business intelligence. In reality, they have been a step in the wrong direction for analytics and data science maturity.

Last week Domino celebrated data science innovators driving breakthroughs in health and life sciences. This week we announce a new slate of innovators in the Financial Services and Insurance edition of the Data Science Innovator’s Playbook, available now as a free download. As before, each of its top data science innovation leaders in the financial services and insurance industries share insights on their work, careers, and the data science profession. The playbook includes seven profiles, the first being of Dr. Tiffany Perkins-Munn.