Subject archive for "mlops"

Now, we are excited to announce Domino Data Lab's scores in the 2024 Gartner Critical Capabilities for Data Science and Machine Learning Platforms, Machine Learning Engineering.

Explore the Generative AI enterprise integration challenges, the nuances of LLMOps, and how Domino's platform aids in responsible AI deployment.

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.

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.

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.

With the increased adoption of cloud computing and demand for managed applications and services, we are now able to offer a new choice of Software as a service (SaaS) deployment model for customers who need it. Let’s explore the organizational components that influence whether SaaS is the right choice for your organization when it comes to data science and MLOps.