Successfully training AI and ML models relies not only on large quantities of data, but also on the quality of their annotations. Data annotation accuracy directly impacts the accuracy of a model and the reliability of its predictions. This is where human-annotated datasets come into play. Human-annotated datasets offer a level of precision, nuance, and contextual understanding that automated methods struggle to match.

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.

An MLOps platform enables streamlining and automating the entire ML lifecycle, from model development and training to deployment and monitoring. This helps enhance collaboration between data scientists and developers, bridge technological silos, and ensure efficiency when building and deploying ML models, which brings more ML models to production faster.

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.

Building a smart call center app based on generative AI is a promising solution for improving the customer experience and call center efficiency. But developing this app requires overcoming challenges like scalability, costs and audio quality. By building and orchestrating an ML pipeline with MLRun, which includes steps like transcription, masking PII and analysis, data science teams can use LLMs to analyze audio calls from their call centers. In this blog post, we explain how.

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 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.