The Challenge with LLMs With the explosion of generative AI tools available for providing information, making recommendations, or creating images, LLMs have captured the public imagination. Although we cannot expect an LLM to have all the information we want, or sometimes even include inaccurate information, consumer enthusiasm for using generative AI tools continues to build. When applied to a business scenario, however, the tolerance for models that provide incorrect or missing answers rapidly approaches 0%. We are quickly learning that broad, generic LLMs are not suitable for domain-specifi ..read more

If you’ve been following our news, you know we just announced free fractional GPU capabilities for open source users, enabling multi-tenancy for NVIDIA GPUs and allowing users to optimize their GPU utilization to support multiple AI workloads as part of our open source and free tier offering. With this latest open source release, you can now optimize your organization’s compute utilization by partitioning GPUs, run more efficient HPC and AI workloads, and get better ROI from your current AI infrastructure and GPU investments. Global enterprise AI projects are being slowed and stalled by legacy ..read more

Unveiling Future Landscapes, Key Insights, and Business Benchmarks In our latest research, conducted this year with AIIA and FuriosaAI, we wanted to know more about global AI Infrastructure plans, including respondents’: 1) Compute infrastructure growth plans 2) Current scheduling and compute solutions experience, and 3) Model and AI framework use and plans for 2024.  Read on to dive into key findings! Download the survey report now → Key Findings 1. 96% of companies plan to expand their AI compute capacity and investment with availability, cost, and infrastructure challenges weighin ..read more

Now you can create and manage your control plane on-prem or on-cloud, regardless of where your data and compute are. We recently announced extensive new orchestration,scheduling, and compute management capabilities for optimizing control of enterprise AI & ML. Machine learning and DevOps practitioners can now fully utilize GPUs for maximal usage with minimal costs. Now, with GPU fractioning/MIG offering, multiple workloads can share the compute power of a single GPU based on priorities, time slicing ratios, or upper hard limits. Global ML teams using these capabilities report they can bett ..read more

Adopting and deploying Generative AI within your organization is pivotal to driving innovation and outsmarting the competition while at the same time, creating efficiency, productivity, and sustainable growth. Acknowledging that AI adoption is not a one-size-fits-all process, each organization will have its unique set of use cases, challenges, objectives, and resources. The framework below tries to recognize this diversity and provides foundational pillars and key considerations to take into account when planning for effective adoption and deployment of Generative AI within your organization ..read more

This tutorial shows how to use ClearML to manage MONAI experiments. Originating from a project co-founded by NVIDIA, MONAI stands for Medical Open Network for AI. It is a domain-specific open-source PyTorch-based framework for deep learning in healthcare imaging. This blog shares how to use the ClearML handlers in conjunction with the MONAI Toolkit.  To view our code example, visit our GitHub page.   It’s easy to use ClearML handlers with the MONAI Toolkit to accelerate AI development for medical workflows.  For technical information on the integration, check out our documentat ..read more

In one of our recent blog posts, about six key predictions for Enterprise AI in 2024, we noted that while businesses will know which use cases they want to test, they likely won’t know  which ones will deliver ROI against their AI and ML investments. That’s problematic, because in our first survey this year, we found that 57% of respondents’ boards expect a double-digit increase in revenue from AI/ML investments in the coming fiscal year, while 37% expect a single-digit increase.  That’s one of the reasons why critical prioritization is key to selecting the right use cases for implem ..read more

Large Language Models (LLMs) have now evolved to include capabilities that simplify and/or augment a wide range of jobs.  As enterprises consider wide-scale adoption of LLMs for use cases across their workforce or within applications, it’s important to note that while foundation models provide logic and the ability to understand commands, they lack the core knowledge of the business. That’s where fine-tuning becomes a critical step.  This blog explains how to attain the highest-performing LLM for your use case through the process of fine-tuning an available open source model with you ..read more

As we head into 2024, AI continues to evolve at breakneck speed. The adoption of AI in large organizations is no longer a matter of “if,” but “how fast.” Companies have realized that harnessing the power of AI is not only a competitive advantage but also a necessity for staying relevant in today’s dynamic market. In this blog post, we’ll look at AI within the enterprise and outline six key predictions for the coming year. At glance, they are: Companies Will Be Shocked at the Cost of Generative AI Prompt Engineering Will Not Be the Be-All and End-All of Gen AI Everyone Works in AI Now Smaller ..read more

To ensure a frictionless AI/ML development lifecycle, ClearML recently announced extensive new capabilities for managing, scheduling, and optimizing GPU compute resources. This capability benefits customers regardless of whether their setup is on-premise, in the cloud, or hybrid. Under ClearML’s Orchestration menu, a new Enterprise Cost Management Center enables customers to better visualize and oversee what is happening in their clusters. The ability to view resource utilization in real time offers teams a better way to manage GPU allocation, queues, job scheduling, and usage, as well as proj ..read more