Cloud Computing

In enterprises, departments often describe their data assets using terminology in silos, and frequently having different interpretations of the same term. This can lead to miscommunication and inconsistent understanding of the enterprise’s data assets. Moreover, some of these terms can be technical (based on the analysis required to arrive at them), making it difficult for […]

Diverse, high-quality data is a prerequisite for reliable, effective, and ethical AI solutions.

Today, we’re excited to announce the preview of our new G4 VMs based on NVIDIA RTX PRO 6000 Blackwell Server edition — the first cloud provider to do so. This follows the introduction earlier this year of A4 and A4X VMs powered by NVIDIA Blackwell GPUs, designed for large-scale AI training and serving. At the

Datadog and Google Cloud have long provided customers with powerful capabilities that enable performant, scalable, and differentiated applications in the cloud; in the past two years alone, Datadog’s revenue on Google Cloud Marketplace has more than doubled. As these customers bring Google Cloud’s AI capabilities into their technology stacks, they require observability tools that allow

Ryan and Ben welcome Tulsee Doshi and Logan Kilpatrick from Google’s DeepMind to discuss the advanced capabilities of the new Gemini 2.5, the importance of feedback loops for model improvement and reducing hallucinations, the necessity of great data for advancements, and enhancing developer experience through tool integration.

Today, we’re excited to announce the general availability of the memory-optimized machine series: Compute Engine M4, our most performant memory-optimized VM with under 6TB of memory.  The M4 family is designed for workloads like SAP HANA, SQL Server, and in-memory analytics that benefit from higher memory-to-core ratio. The M4 is based on Intel’s latest 5th