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.
The Data Disconnect: A Key Challenge for Machine Learning Deployment
March 25, 2024 by Leave a Comment
Heard on the Street – 3/21/2024
March 21, 2024 by Leave a Comment
Welcome to insideBIGDATA’s “Heard on the Street” round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace.
NVIDIA Blackwell Platform Arrives to Power a New Era of Computing
March 19, 2024 by Leave a Comment
GTC 2024—Powering a new era of computing, NVIDIA announced that the NVIDIA Blackwell platform has arrived — enabling organizations everywhere to build and run real-time generative AI on trillion-parameter large language models at up to 25x less cost and energy consumption than its predecessor.
The Five Step Playbook to Move GenAI into Production
March 18, 2024 by Leave a Comment
In this contributed article, Josh Reini, Developer Relations Data Scientist, TruEra, discusses how gaining the required confidence to deploy GenAI apps at scale can be challenging, and structured evaluation has gained recognition as a key requirement on the path from science experiment to customer value. Evaluation frameworks can play a critical role in this journey by allowing developers to run experiments faster and gain systematic validation for production readiness. Connecting such an evaluation framework with a scaled observability platform brings confidence in production. This article explores five practical steps to move LLM applications from early prototypes to scaled, production applications.
How Optical I/O is Enabling the Future of Generative AI: A Q&A with Ayar Labs CTO Vladimir Stojanovic
March 13, 2024 by Leave a Comment
As we look at the future of AI and the challenges it faces, who better to provide insights than Vladimir Stojanovic, CTO and co-founder of Ayar Labs. In this Q&A interview, we’ve asked Vladimir a dozen questions about how Ayar Labs’ technology is enabling the growth of generative AI.
Fine-Tune Your LLMs or Face AI Failure
March 11, 2024 by Leave a Comment
In this contributed artticle, Dr. Muddu Sudhakar, CEO and Co-founder of Aisera, focuses on the downsides of general-purpose Gen AI platforms and why enterprises can derive more value from a fine-tuned model approach.
Heard on the Street – 3/7/2024
March 7, 2024 by Leave a Comment
Welcome to insideBIGDATA’s “Heard on the Street” round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace.
Hammerspace Unveils the Fastest File System in the World for Training Enterprise AI Models at Scale
March 4, 2024 by Leave a Comment
Hammerspace, the company orchestrating the Next Data Cycle, unveiled the high-performance NAS architecture needed to address the requirements of broad-based enterprise AI, machine learning and deep learning (AI/ML/DL) initiatives and the widespread rise of GPU computing both on-premises and in the cloud. This new category of storage architecture – Hyperscale NAS – is built on the tenants required for large language model (LLM) training and provides the speed to efficiently power GPU clusters of any size for GenAI, rendering and enterprise high-performance computing.
Securing GenAI in the Enterprise
February 28, 2024 by Leave a Comment
Opaque Systems released a new whitepaper titled “Securing GenAI in the Enterprise.” Enterprises are chomping at the bit to use GenAI to their benefit but they are stuck. Data privacy is the number one factor that stalls GenAI initiatives. Concerns about data leaks, malicious use, and ever-changing regulations loom over the exciting world of Generative AI (GenAI), specifically large language models (LLMs).
A Brief Overview of the Strengths and Weaknesses Artificial Intelligence
February 26, 2024 by Leave a Comment
In this contributed article, editorial consultant Jelani Harper suggests that since there are strengths and challenges for each form of AI, prudent organizations will combine these approaches for the most effective results. Certain solutions in this space combine vector databases and applications of LLMs alongside knowledge graph environs, which are ideal for employing Graph Neural Networks and other forms of advanced machine learning.
