At Cloudera, we continuously strive to empower organizations to unlock the full potential of their data, catalyzing innovation and driving actionable insights. And so we are thrilled to introduce our latest applied ML prototype (AMP)—a large language model (LLM) chatbot customized with website data using Meta’s Llama2 LLM and Pinecone’s vector database.

Data integration is essential for analytics, regression analysis and your first forays into generative AI.

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.

In the vast realm of machine learning, it’s well-known that data is the lifeblood that drives model performance. Yet, as we dive deeper into the intricacies of machine learning, a pertinent question arises: Is it just about accumulating vast amounts of data?

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.

While AI and machine learning have been industry buzzwords for a while, they are now becoming fundamental to software testing. Machine learning algorithms are making it easier to sift through logs, identify patterns, and even predict where bugs are most likely to occur. As these technologies mature, the role of AI in software testing will undoubtedly expand.