Generative AI Must Be Responsible AI. What You Need to Know.

Companies diving head-first into Generative AI must consider the risks it brings. Domino’s guide shows you how to harness GenAI responsibly. Download now!

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.

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.

Generative AI transforms industries, but LLM deployment is tough. See how Domino simplifies LLM hosting & inference.

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.