If you’ve been following our news, you know we just announced free fractional GPU capabilities for open source users, enabling multi-tenancy for NVIDIA GPUs and allowing users to optimize their GPU utilization to support multiple AI workloads as part of our open source and free tier offering.

Think back just a few years ago when most enterprises were either planning or just getting started on their cloud journeys. The pandemic hit and, virtually overnight, the need to radically change ways of working pushed those cloud journeys into overdrive. Cost-effective adaptability was essential. And the companies that could scale up or scale down quickly were the ones that navigated the pandemic successfully. Migrating to the cloud made that possible.

The potential impact of AI in banking appears boundless. A 2023 McKinsey report found that effectively incorporating generative AI tools into business operations could lead to annual operational savings ranging from $200 billion to $340 billion for the global financial services industry. These cutting-edge technologies can also enhance customer satisfaction, attract more potential customers, and improve employee experience.

In just a few months, enterprise AI has rapidly evolved into a strategic partner for organizations that want to stay ahead of the disruption curve. Whether enterprises are leveraging predictive analytics for strategic decision-making, using machine learning algorithms for supply chain optimization, or applying generative AI and large language models to enhance customer service, it’s clear AI will help business in many ways.

The editorial team of Hitachi Vantara sat down for an interview with our Chief Product Officer, Octavian Tanase, to learn about his vision and strategy for AI in the enterprise.

You can now access Gemini 1.0 Pro from inside the BigQuery console, gaining scale and efficiency to ground LLMs with your business data.

Founders of the world’s leading data companies share their insights on the latest trends in their industry and the keys to turning generative AI into business impact.

GPU as a Service (GPUaaS) serves as a cost-effective solution for organizations who need more GPUs for their ML and gen AI operations. By optimizing the use of existing resources, GPUaaS allows organizations to build and deploy their applications, without waiting for new hardware. In this blog post, we explain how GPUaaS as a service works, how it can close the GPU shortage gap, when to use GPUaaS and how it fits with gen AI.

We consistently hear from engineering leaders that automated test maintenance is a painful, mindless exercise that takes too much time away shipping code — the main goal of any startup software team. Our vision is to deliver end-to-end test automation that requires no maintenance from your team. With that in mind, we’ve designed Rainforest as an intuitive, no-code platform that anyone can quickly use with no training. This has been an important — but insufficient! — step.