Blog archive, page 6
Taming Model Sprawl with Domino Model Registry
At Rev4, Domino recently announced the launch of Domino Model Sentry, a tightly integrated set of capabilities for building and operating AI responsibly at scale. With Domino Model Sentry, organizations can closely and continuously manage all aspects of AI throughout the entire lifecycle. This article will focus specifically on a core capability of Domino Model Sentry, Model Registry.
By Tim Law7 min read
Domino’s New Cost Governance Capabilities for AI Drive Accountability, Visibility & Savings
Controlling and tracking AI, data science and related compute costs are often manual and error prone, and require tagging specific infrastructure to distributed IT workloads. Domino.ai provides the governance guardrails necessary to reduce and control infrastructure costs, including expensive high-performance compute (e.g. GPUs) resources.
By Nikhil Jethava6 min read
Crossing the Frontier: LLM Inference on Domino
Generative AI transforms industries, but LLM deployment is tough. See how Domino simplifies LLM hosting & inference.
By Subir Mansukhani10 min read
Breaking Generative AI Barriers with Efficient Fine-Tuning Techniques
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.
By Subir Mansukhani9 min read
Beyond the Hype: Domino Offers Production-Ready Generative AI Powered by NVIDIA
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.
By David Schulman9 min read
Plug AI’s Silent Drain: How to deliver AI cost-effectively and achieve 722%* ROI
As large enterprises begin to adopt and embrace Large Language Models (LLMs) and seek enterprise-wide AI adoption that transforms every aspect of their business, data science executives are realizing that building a large team and hiring top talent simply isn’t enough to create a broad and bottom-line impact.
By Leila Nouri8 min read
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