Blog archive, page 6

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.

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.

Recently at Rev4, Domino announced the launch of Domino Nexus for Domino Cloud as well as a specialized Domino Cloud for Life Sciences — our fully-managed private SaaS products. The addition of Domino Nexus gives Domino Cloud customers a single pane of glass where they can run AI and machine learning workloads across any infrastructure — in any cloud(s), region, or on premises. Domino Nexus unifies data science silos across a customer’s infrastructure assets, so they have one place to build, deploy, and monitor models.