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.
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.
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.
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.
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.
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.