Next-gen search and RAG with Vertex AI
Generative AI has fundamentally transformed how the world interacts with information, and the search industry is no exception. The search landscape is changing rapidly, driven by the rise of large language models (LLMs). Whether they’re interacting with their company’s internal data or browsing public websites,…
Experimenting with Gemini 1.5 Pro and vulnerability detection
Unpatched software vulnerabilities can have serious consequences. Google Cloud want developers to reduce the risks they face by focusing on developing code that is secure by design and secure by default. While secure development can be time-consuming, generative AI can be used responsibly to help…
Cut costs and boost efficiency with Dataflow’s new custom source reads
Scaling workloads often comes with a hefty price tag, especially in streaming environments, where latency is heavily scrutinized. So it makes sense Google want Google pipelines to run without bottlenecks — because costs and latency grow with inefficiencies! This is especially true for most modern…
The strategy for effectively optimizing cloud billing cost management
Effective cloud cost management is important for reasons beyond cost control. A good cloud cost management solution provides you the ability to reduce waste and predictably forecast both costs and resource needs. Why is cloud cost management a challenge for most businesses? For all of…
Get to know Vertex AI Model Monitoring
Google are introducing the new Vertex AI Model Monitoring, a re-architecture of Vertex AI’s model monitoring features, to provide a more flexible, extensible, and consistent monitoring solution for models deployed on any serving infrastructure (even outside of Vertex AI, e.g. Google Kubernetes Engine, Cloud Run,…
Google Cloud expands grounding capabilities on Vertex AI
Introduced in April, Vertex AI Agent Builder gathers all the surfaces and tools developers need to build enterprise-ready generative AI experiences, apps, and agents. Some of the most powerful tools are the components for retrieval augmented generation (RAG), and the unique ability to ground Gemini outputs with…
Making Vertex AI the most enterprise-ready generative AI platform
It’s been amazing to see the impressive things customers are doing with generative AI and agents. Less than three months ago, Google shared 101 real-world gen AI use cases from the world’s leading organizations. Since then, to enable businesses to roll out compelling AI agents faster,…
Datastream’s SQL Server source is generally available
Have you ever wished it were easier to replicate your SQL Server databases? To address this challenge, Google are announcing the general availability (GA) of Datastream’s SQL Server source. This means you can now easily and reliably replicate data from your SQL Server databases to BigQuery, Cloud…
New strides in making AI accessible for every enterprise
Google have been thrilled to see the recent enthusiasm and adoption of Gemini 1.5 Flash — Google fastest model to date, optimized for high-volume and high-frequency tasks at scale. Every day, Google learn about how people are using Gemini to do amazing things like transcribe audio, understand code errors, and build…
Designing Generative AI Solutions: Key Lessons Learned
Generative AI is transforming the way we interact with technology. Google Cloud Applied AI Engineering team has shaped the design and development of generative AI solutions for Generative AI for Marketing, Customer Experience Modernization, and Open Data QnA (NL2SQL), among others. Throughout this process, Google have gained valuable…
How to build user authentication into your gen AI app-accessing database
Generative AI agents introduce immense potential to transform enterprise workspaces. Enterprises from almost every industry are exploring the possibilities of generative AI, adopting AI agents for purposes ranging from internal productivity to customer-facing support. However, while these AI agents can efficiently interact with data already…
Enhancing LLM quality and interpretability with the Vertex Gen AI Evaluation Service
Developers harnessing the power of large language models (LLMs) often encounter two key hurdles: managing the inherent randomness of their output and addressing their occasional tendency to generate factually incorrect information. Somewhat like rolling dice, LLMs offer a touch of unpredictability, generating different responses even…