As generative AI grows in popularity and enterprises scramble to embrace new technology in a scalable, compliant way, it can be difficult to know the right path forward. Your enterprise has already invested in automating processes to free up resources and improve organizational efficiency, but is it enough? This is where intelligent process automation comes into play.

In the last couple of years, AI has launched itself to the forefront of technology initiatives across industries. In fact, Gartner predicts the AI software market will grow from $124 billion in 2022 to $297 billion in 2027. As a data platform company, Cloudera has two very clear priorities. First, we need to help customers get AI models based on trusted data into production faster than ever.

Using BigQuery object tables, we analyze unstructured Cloud Storage data with generative AI models and join the results with BigQuery structured data.

Generative AI (GenAI) has taken the world by storm, promising to revolutionize industries and transform the way businesses operate. From generating creative content to automating complex tasks, the potential applications of GenAI are vast and exciting. However, implementing GenAI in an enterprise setting comes with its own set of challenges. At Cloudera, we understand the complexities of enterprise GenAI adoption.

In this latest entry of our fascinating interview series that focuses on major players in the global tech arena, we are delighted to present Miguel Jetté, Vice President of Artificial Intelligence at Rev. Join us as we dive into his career development, provide tips for aspiring AI leaders, and discuss the key lessons he has learned along the way.

I have been having quite a bit of fun in this blog series exploring the “sliding doors” (or divergent paths) that organizations can take with their AI and data initiatives. The right door can lead to great value, as I covered in my previous posts on ESG reporting, data products, and responsible AI.

We did a sort of time travel on 20th April at QonfX. If you are not one of the 3000+ people who registered for this event, it is a unique software testing conference that keeps its focus on the Future of Testing. This year was the second edition of QonfX and received even more love than the last time. Feedback like the above filled our social feeds during and post QonfX. We cannot keep a count of the number of times attendees used the words ‘eye-opening’ for the talks given by the speakers.

It is a fairly recent trend for companies to amass GPU firepower to build their own AI computing infrastructure and support the growing number of compute requests. Many recent AI tools now enable data scientists to work on data, run experiments, and train models seamlessly with the ability to submit their jobs and monitor their progress. However, for many organizations with mature supercomputing capabilities, Slurm has been the scheduling tool of choice for managing computing clusters.

Our early roadmap in 2024 has been largely focused on improving orchestration and compute infrastructure management capabilities. Last month we released a Resource Allocation Policy Management Control Center with a new, streamlined UI to help teams visualize their compute infrastructure and understand which users have access to what resources.

Snowflake Cortex is a fully-managed service that enables access to industry-leading large language models (LLMs) is now generally available. You can use these LLMs in select regions directly via LLM Functions on Cortex so you can bring generative AI securely to your governed data. Your team can focus on building AI applications, while we handle model optimization and GPU infrastructure to deliver cost-effective performance.