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.

Read About The Hidden Costs, Challenges, and Total Cost of Ownership of Generative AI Adoption in the Enterprise as Well as C-level Key Considerations, Challenges and Strategies for Unleashing AI at Scale ClearML recently conducted two global survey reports with the AI Infrastructure Alliance (AIIA) on the business adoption of Generative AI. We surveyed 1,000 AI Leaders and C-level executives in charge of spearheading Generative AI initiatives within their organizations.

Generative AI (GenAI) has the potential to transform enterprise product operations, and as a Chief Product Officer (CPO), it’s essential to understand how to leverage generative AI to drive success within your product organization. This article serves as a comprehensive guide for how CPOs can use GenAI in product strategy, design, and innovation – generating new product ideas, creating unique designs, and exploring different variations and options.