Systems | Development | Analytics | API | Testing

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.

In our latest research, conducted this year with AIIA and FuriosaAI, we wanted to know more about global AI Infrastructure plans, including respondents’: 1) Compute infrastructure growth plans 2) Current scheduling and compute solutions experience, and 3) Model and AI framework use and plans for 2024. Read on to dive into key findings! Download the survey report now →

When ChatGPT first hit the market its human-like responses were astonishing, yet somewhat eerie. Many thought, “It communicates just like me.” It follows that the next logical thought was, “It’s going to take over my job.” However, the fear that AI-driven tools will replace human software testers is unfounded. AI will certainly impact testing jobs, including market expectations, skillsets, and required knowledge.