Vertex AI grounding: More reliable models, fewer hallucinations
Google Cloud showcased how generative AI is transforming the way enterprises work. Across all the customer, one thing was clear – if last year was about gen AI exploration and experimentation, this year is about achieving real-world impact. Gen AI has the potential to revolutionize…
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,…
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…
Long document summarization with Workflows and Gemini models
With generative AI top of mind for both developers and business stakeholders, it’s important to explore how products like Workflows, Google Cloud’s serverless execution engine, can automate and orchestrate large language model (LLM) use cases. We recently covered how to orchestrate Vertex AI’s PaLM and Gemini APIs…
Introducing Security Command Center protection for Vertex AI
As developers use Vertex AI, Google Cloud’s end-to-end AI platform that includes intuitive tooling to build the next generation of AI applications, IT teams are called on to bolster cloud infrastructure security. Aligned with the Secure AI Framework (SAIF) we introduced last year, Google want to share approaches…
No more double vision: How Miinto improved its customer experience using Vertex AI Vision
The fashion world is notoriously fast-paced, and it can be hard to keep up with the latest trends. At Miinto, we bring over 1,000 of the world’s best boutiques together in one place. Google strive to offer the most customer-centric fashion platform on the planet,…
Vertex AI extends enterprise-ready generative AI development with new models, tooling
Since announcing Generative AI support on Vertex AI less than six months ago, Google have been thrilled and humbled to see innovative use cases from customers of all kinds — from enterprises like GE Appliances, whose consumer app SmartHQ offers users the ability to generate custom recipes…
Streamline your models to production with the Vertex AI Model Registry
Machine learning (ML) is iterative in nature — model improvement is a necessity to drive the best business outcomes. Yet, with the proliferation of model artifacts, it can be difficult to ensure that only the best models make it into production. Data science teams may…
Building a scalable MLOps system with Vertex AI AutoML and Pipeline
When you build a Machine Learning (ML) product, consider at least two MLOps scenarios. First, the model is replaceable, as breakthrough algorithms are introduced in academia or industry. Second, the model itself has to evolve with the data in the changing world. Google can handle both scenarios…
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- Vertex AI grounding: More reliable models, fewer hallucinations
Recent Posts
- Find key insights with contribution analysis in BigQuery ML
- Secure your data ecosystem: a multi-layered approach with Google Cloud
- Gemini for Google Workspace now supports additional languages
- 5 ways small business owners can use Gemini for Workspace to plan for the holiday season and New Year
- Vertex AI grounding: More reliable models, fewer hallucinations
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