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.
Better vibes and vibe coding with Gemini 2.5 Read More »
In the dynamic world of beauty retail, staying ahead requires more than just the hottest trends — it demands agility, data-driven insights, and seamless customer experiences. Ulta Beauty, a leader in the beauty and wellness industry, understands this. Building on the success of modernizing its e-commerce platform with Google Kubernetes Engine (GKE), Ulta Beauty partnered
Ulta Beauty redefines beauty retail with BigQuery Read More »
Today, we’re introducing Pub/Sub Single Message Transforms (SMTs) to make it easy to perform simple data transformations right within Pub/Sub itself. This comes at a time when businesses are increasingly reliant on streaming data to derive real-time insights, understand evolving customer trends, and ultimately make critical decisions that impact their bottom line and strategic direction.
Simplify your streaming pipelines with new Pub/Sub Single Message Transforms Read More »
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
Unlock 66% better price-performance with new M4 VMs for memory-intensive workloads Read More »
The pace of innovation in open-source AI is breathtaking, with models like Meta’s Llama4 and DeepSeek AI’s DeepSeek. However, deploying and optimizing large, powerful models can be complex and resource-intensive. Developers and machine learning (ML) engineers need reproducible, verified recipes that articulate the steps for trying out the models on available accelerators. Today, we’re excited
Accelerate your gen AI: Deploy Llama4 & DeepSeek on AI Hypercomputer with new recipes Read More »
Looking to fine-tune multimodal AI models for your specific domain but facing infrastructure and implementation challenges? This guide demonstrates how to overcome the multimodal implementation gap using Google Cloud and Axolotl, with a complete hands-on example fine-tuning Gemma 3 on the SIIM-ISIC Melanoma dataset. Learn how to scale from concept to production while addressing the
Building a Production Multimodal Fine-Tuning Pipeline Read More »
Kathleen Vignos, VP of Software Engineering at Capital One, sits down with Ryan to explore shifting to 100% serverless architecture in enterprise, deploying talent for better customer experience, and fostering AI innovation and tech advancements in a regulated banking environment.
Banking on a serverless world Read More »
BigQuery provides a powerful platform for analyzing large-scale datasets with high performance. However, as data volumes and query complexity increase, maintaining operational efficiency is essential. BigQuery workload management provides comprehensive control mechanisms to optimize workloads and resource allocation, preventing performance issues and resource contention, especially in high-volume environments. And today, we’re excited to announce several
Maximize BigQuery performance with enhanced workload management Read More »
Today, we are excited to announce that Gartner® has named Google as a Leader in the 2025 Magic Quadrant™ for Data Science and Machine Learning Platforms report (DSML). We believe that this recognition is a reflection of continued innovations to address the needs of data science and machine learning teams, as well as new types
