Subject archive for "enterprise-mlops"

True reproducibility means anyone can return to a point in time — anywhere in the AI/ML lifecycle — and see how a model was built and understand its purpose and KPIs. Yet, most AI models are built outside of controlled environments and systems of record. Enterprise AI platforms like Domino solve this by automatically unifying and capturing all model provenance and all artifacts across teams, users, tools, and environments without manual detective work, which can produce mixed results.

AI integration brings many IT challenges. A BARC report offers the insights you need.

Domino's new AI Gateway enables secure, governed, and seamless access to external Large Language Models (LLMs) for building Generative AI applications responsibly and cost-effectively,

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

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.

Today Domino announced a revolutionary new capability that enables data science leaders to expand their talent pool, promote a culture of analytics around the enterprise, increase collaboration across teams, and ultimately deliver more value to the business.