Source URL: https://cloud.google.com/blog/products/ai-machine-learning/what-google-cloud-announced-in-ai-this-month/
Source: Cloud Blog
Title: News you can use: What we announced in AI this month
Feedly Summary: 2025 is off to a racing start. From announcing strides in the new Gemini 2.0 model family to retailers accelerating with Cloud AI, we spent January investing in our partner ecosystem, open-source, and ways to make AI more useful. We’ve heard from people everywhere, from developers to CMOs, about the pressure to adapt the latest in AI with efficiency and speed – and the delicate balance of being both conservative and forward-thinking. We’re here to help. Each month, we’ll post a retrospective that recaps Google Cloud’s latest announcements in AI – and importantly, how to make the most of these innovations.
Top announcements: Bringing AI to you
This month, we announced agent evaluation in Vertex AI. A surprise to nobody, AI agents are top of mind for many industries looking to deploy their AI and boost productivity. But closing the gap between impressive model demos and real-world performance is crucial for successfully deploying generative AI. That’s why we announced Vertex AI’s RAG Engine, a fully managed service that helps you build and deploy RAG implementations with your data and methods. Together, these new innovations can help you build reliable, trustworthy models.
From an infrastructure perspective, we announced new updates to AI Hypercomputer. We wanted to make it easier for you to run large multi-node workloads on GPUs by launching A3 Ultra VMs and Hypercompute Cluster, our new highly scalable clustering system. This builds on multiple advancements in AI infrastructure, including Trillium, our sixth-generation TPU.
aside_block
What’s new in partners and open-source
This month, we invested in our relationship with our partners. We shared how Gemini-powered content creation in Partner Marketing Studio will help partners co-market faster. These features are designed to streamline marketing efforts across our entire ecosystem, empowering our partners to unlock new levels of success, efficiency, and impact.
At the same time, we shared several important announcements in the world of open-source. We announced Mistral AI’s Mistral Large 24.11 and Codestral 25.01 models on Vertex AI. These models will help developers write code and build faster – from high-complexity tasks to reasoning tasks, like creative writing. To help you get started, we provided sample code and documentation.
And, most recently, we announced the public beta of Gen AI Toolbox for Databases in partnership with LangChain, the leading orchestration framework for developers building LLM applications. Toolbox is an open-source server that empowers application developers to connect production-grade, agent-based generative AI applications to databases. You can get started here.
Industry news: Google Cloud at the National Retail Federation (NRF)
The National Retail Federation kicked off the year with their annual NRF conference, where Google Cloud showed how AI agents and AI-powered search are already helping retailers operate more efficiently, create personalized shopping experiences, and use AI to get the latest products and experiences to their customers. Check our new AI tools to help retailers build gen AI search and agents.
As an example, Google Cloud worked with NVIDIA to empower retailers to boost their customer engagements in exciting new ways, deliver more hyper-personalized recommendations, and build their own AI applications and agents. Now with NVIDIA’s AI Enterprise software available on Google Cloud, retailers can handle more data and more complex AI tasks without their systems getting bogged down.
News you can use
This month, we shared several ways to better implement fast-moving AI, from a comprehensive guide on Supervised Fine Tuning (SFT), to how developers can help their LLMs deliver more accurate, relevant, and contextually aware responses, minimizing hallucinations and building trust in AI applications by optimizing their RAG retrieval.
We also published new documentation to use open models in Vertex AI Studio. Model selection isn’t limited to Google’s Gemini anymore. Now, choose models from Anthropic, Meta, and more when writing or comparing prompts.
Hear from our leaders
We closed out the month with The Prompt, our monthly column that brings observations from the field of AI. This month, we heard from Warren Barkley, AI product leader, who shares some best practices and essential guidance to help organizations successfully move AI pilots to production. Here’s a snippet:
More than 60% of enterprises are now actively using gen AI in production, helping to boost productivity and business growth, bolster security, and improve user experiences. In the last year alone, we witnessed a staggering 36x increase in Gemini API usage and a nearly 5x increase of Imagen API usage on Vertex AI — clear evidence that our customers are making the move towards bringing gen AI to their real-world applications.
Stay tuned for monthly updates on Google Cloud’s AI announcements, news, and best practices. For a deeper dive into the latest from Google Cloud, read our weekly updates, The Overwhelmed Person’s Guide to Google Cloud.
AI Summary and Description: Yes
Summary: The text highlights recent advancements in Google’s AI offerings and infrastructure, particularly focusing on Vertex AI and its integrations. It addresses the ongoing push for businesses to adopt AI efficiently, outlining innovations aimed at improving AI deployment, infrastructure scalability, and partner collaborations, which are critical for professionals in AI, cloud computing, and infrastructure security.
Detailed Description:
The content emphasizes Google’s efforts in advancing AI technologies and improving cloud infrastructure to support various industries. Here are the major points of interest:
– **AI Model Advancements**: Introduction of the Gemini 2.0 model family and enhancements to Vertex AI, particularly focusing on agent evaluation and the RAG Engine.
– AI agents are becoming increasingly essential for various industries as businesses look to enhance productivity.
– The RAG Engine aims to bridge the gap between model demonstration and real-world application, which is crucial for effective deployment of generative AI.
– **Infrastructure Enhancements**: Announcement of new infrastructure capabilities, such as A3 Ultra VMs and Hypercompute Cluster.
– These improvements facilitate running large workloads on GPUs, indicating a significant upgrade in AI computational efficiency.
– The mention of advancements in AI infrastructure, such as the sixth-generation TPU (Trillium), underlines Google’s commitment to enhancing processing power for AI applications.
– **Partnerships and Open Source**: Strengthening relationships with partners and making strides in open-source developments.
– Gemini-powered features for Partner Marketing Studio are aimed at improving cooperative marketing outcomes.
– Introduction of new models such as Mistral Large and Codestral on Vertex AI that assist developers in coding and creative tasks, enhancing productivity.
– The Gen AI Toolbox for Databases, in partnership with LangChain, is noteworthy as it connects generative AI applications to databases, promoting more sophisticated application development.
– **Industry Application**: Participation in events such as the National Retail Federation (NRF) to demonstrate AI applications in retail.
– Collaborations with major tech players like NVIDIA indicate a drive to innovate customer engagement through AI, enhancing the user experience with personalized interactions.
– **Practical Insights for Developers**: Provision of guides and resources to implement AI effectively, including topics on Supervised Fine Tuning and optimizing LLM responses.
– These resources are essential for minimizing AI-related errors (hallucinations) and enhancing the accuracy of AI applications.
– **Market Trends**: The text reflects significant adoption rates of generative AI among enterprises, citing a 36x increase in API usage, which signals a robust inclination towards practical AI applications in businesses.
Overall, the update underscores the increasing integration of AI in cloud capabilities, development tools, and industry applications, which are crucial for security and compliance professionals focused on the evolving landscape of AI and infrastructure. The insights provided are valuable for understanding market trends and best practices in AI deployment.