Source URL: https://cloud.google.com/blog/products/ai-machine-learning/gartner-2025-magic-quadrant-for-data-science-and-ml-platforms/
Source: Cloud Blog
Title: Google is a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms report
Feedly Summary: 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 of practitioners working alongside data scientists in the dynamic space of generative AI.
Download the complimentary 2025 Gartner Magic Quadrant™ for Data Science and Machine Learning Platforms.
AI is driving a radical transformation in how organizations operate, compete, and innovate. Working closely with customers, we’re delivering the innovations for a unified data and AI platform to meet the demands of the AI era, including data engineering and analysis, data science, MLOps, gen AI application and agent development tools, and a central layer of governance.
Unified AI platform with best-in-class multimodal AI
Google Cloud offers a wide spectrum of AI capabilities, starting from the foundational hardware like Tensor Processing Units (TPUs) to AI agents, and the tools for building them. These capabilities are powered by our pioneering AI research and development, and our expertise in taking AI to production with large-scale applications such as YouTube, Maps, Search, Ads, Workspace, Photos, and more.
All of this research and experience fuels our Vertex AI platform, our unified AI platform for MLOps tooling, predictive and gen AI use cases, that sits at the heart of Google’s DSML offering. Vertex AI provides a comprehensive suite of tools covering the entire AI lifecycle, including data engineering and analysis tools, data science workbenches, MLOps capabilities for deploying and managing models, and specialized features for developing gen AI applications and agents. Moreover, our Self-Deploy capability enables our partners to not only build and host their models within Vertex AI for internal users, but also distribute and commercialize those models. Customer use of Vertex AI has grown 20x in the last year driven by Gemini, Imagen, and Veo models.
Vertex AI Model Garden offers a curated selection of over 200 enterprise-ready models from Google like Gemini, partners like Anthropic, and the open ecosystem. Model Garden helps customers access the highest performing foundation models suited for their business needs and easily customize them with their own data, deploy to applications with just one click, and scale with end-to-end MLOps built-in.
Building on Google DeepMind research, we recently announced Gemini 2.5, our most intelligent AI model yet. Gemini 2.5 models are now thinking models, capable of reasoning (and showing its reasoning) before responding, resulting in dramatically improved performance. Transparent step-by-step reasoning is crucial for enterprise trust and compliance. We also launched Gemini 2.5 Flash, our cost-effective, low-latency workhorse model. Gemini 2.5 Flash will be generally available for all Vertex AI users in early June, with 2.5 Pro generally available soon after.
Vertex AI is now the only platform with generative media models across all modalities — video, image, speech, and music. At Google I/O, we announced several innovations in this portfolio, including the availability of Veo 3, Lyria 2, and Imagen 4 on Vertex AI. Veo 3 combines video and audio generation, taking content generation to a new level. The state-of-the-art model features improved quality when generating videos from text and image prompts. In addition, Veo 3 also generates videos with speech (dialogue and voice-overs) and audio (music and sound effects). Lyria 2, Google’s latest music generation model, features high-fidelity music across a range of styles. And Imagen 4, Google’s highest-quality image generation model, delivers outstanding text rendering and prompt adherence, higher overall image quality across all styles, and multilingual prompt support to help creators globally. Imagen 4 also supports multiple model variants to help customers optimize around quality, speed and cost.
All of this innovation resides on Vertex AI, so that AI projects can reach production and deliver business value while teams collaborate to improve models throughout the development lifecycle.
For instance, customers like Radisson Hotel Group have redefined personalized marketing with Google Cloud. Partnering with Accenture, the global hotel chain leveraged BigQuery, Vertex AI, Google Ads, and Google’s multimodal Gemini models to build a generative AI agent to help create locally relevant ad content and translate it into more than 30 languages — reducing content creation time from weeks to hours. This AI-driven approach has increased team productivity by 50%, boosted return on ad spend by 35%, and driven a 22% increase in ad-driven revenue.
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A new era of multi-agent management
Eventually, we believe that every enterprise will rely on multi-agent systems, including those built on different frameworks or providers. We recently announced multiple enhancements to Vertex AI so you can build agents with an open approach and deploy them with enterprise-grade controls. This includes an Agent Development Kit (ADK), available for Python and Java, with an open-source framework for designing agents built on the same framework that powers Google Agentspace and Google Customer Engagement Suite agents. Many powerful examples and extensible sample agents are readily available in Agent Garden. You can also take advantage of Agent Engine, a fully managed runtime in Vertex AI that helps you deploy your custom agents to production with built-in testing, release, and reliability at global scale.
Connecting all your data to AI
Enterprise agents need to be grounded in relevant data to be successful. Whether helping a customer learn more about a product catalog or helping an employee navigate company policies, agents are only as effective as the data they are connected to. At Google Cloud, we do this by making it easy to leverage any data source. Whether it’s structured data in a relational database or unstructured content like presentations and videos, Google Cloud tools let customers easily use their existing data architectures as retrieval-augmented generation (RAG) solutions. With this approach, developers get the benefits of Google’s decades of search experience from out-of-the-box offerings, or can build their own RAG system with best-in-class components.
For RAG on an enterprise corpus, Vertex AI Search is our out-of-the-box solution that delivers high quality at scale, with minimal development or maintenance overhead. Customers who prefer to fully customize their solution can use our suite of individual components including the Layout Parser to prepare unstructured data, Vertex embedding models to create multimodal embeddings, Vertex Vector Search to index and serve the embeddings at scale, and the Ranking API to optimize the results. And RAG Engine provides an easy way for developers to orchestrate these components, or mix and match with third-party and open-source tools. BigQuery customers can also use its built-in vector search capabilities for RAG, or leverage the new connector with Vertex Vector Search to get the best of both worlds, by combining the data in BigQuery with a purpose-built high performance vector search tool.
Unified data and AI governance
With built-in governance, customers can simplify how they discover, manage, monitor, govern, and use their data and AI assets. Dataplex Universal Catalog brings together a data catalog and a fully managed, serverless metastore, enabling interoperability across Vertex AI, BigQuery, and open-source formats such as Apache Spark and Apache Iceberg with a common metadata layer. Customers can also use a business glossary for a shared understanding of data and define company terms, creating a consistent foundation for AI.
At Google Cloud, we’re committed to helping organizations build and deploy AI and we are investing heavily in bringing new predictive and gen AI capabilities to Vertex AI. For more, download the full 2025 Gartner Magic Quadrant™ for Data Science and Machine Learning Platforms report.
Gartner Magic Quadrant for Data Science and Machine Learning Platforms – Afraz Jaffri, Maryam Hassanlou, Tong Zhang, Deepak Seth, Yogesh Bhatt, May 28, 2025
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AI Summary and Description: Yes
**Short Summary with Insight:**
Google has been recognized as a Leader in the 2025 Gartner Magic Quadrant for Data Science and Machine Learning Platforms, reflecting its significant innovations in AI and MLOps. The advancements in Google Cloud’s Vertex AI platform highlight its capabilities in generative AI, multi-agent systems, and integrated governance, indicating a transformative impact on enterprise operations and data-driven decision-making.
**Detailed Description:**
The text discusses Google’s achievements and advancements in AI and machine learning, particularly through its Vertex AI platform within Google Cloud. This announcement is significant for professionals in the fields of AI, cloud, and infrastructure security as it showcases how integrated AI solutions can drive productivity and compliance.
– **Recognition by Gartner:** Google was named as a Leader in the 2025 Magic Quadrant for Data Science and Machine Learning Platforms report, emphasizing its role in innovating for data science and generative AI applications.
– **Technological Innovations in AI:**
– Google is focused on developing a unified data and AI platform that offers tools for data engineering, MLOps, and generative AI.
– The Vertex AI framework includes comprehensive tools that facilitate the entire AI lifecycle—from data engineering to model deployment and management.
– **MLOps Capabilities:** Vertex AI’s tools are designed to enhance MLOps, enabling enterprises to deploy and manage machine learning models efficiently. The platform supports both internal use and commercialization of AI models.
– **Launch of Novel AI Models:**
– The announcement of Gemini 2.5, an intelligent model capable of reasoning transparently, enhances trust and compliance for enterprises leveraging AI.
– Additional generative models, including Veo 3 and Imagen 4, expand capabilities for multimedia generation, showcasing potential applications in personalized marketing.
– **Enterprise Impact:** A case study involving Radisson Hotel Group illustrates the practical applications of Google Cloud’s AI. The hotel chain improved advertising productivity and reduced content creation time significantly through the utilization of Vertex AI and generative models.
– **Multi-agent Systems Development:**
– The introduction of the Agent Development Kit and multi-agent management enhances the flexibility and capabilities of enterprises to implement AI solutions.
– The framework supports the deployment of agents designed on various platforms while ensuring enterprise-grade control and interoperability.
– **Data Integration and Governance:**
– Google emphasizes the importance of using relevant data for training effective AI agents, promoting the integration of structured and unstructured data sources.
– The Dataplex Universal Catalog feature provides a framework for effective data governance, facilitating monitoring and compliance across AI assets.
Overall, Google’s advancements in the AI space, particularly within the context of MLOps and generative AI, underscore the growing importance of integrated data management and compliance frameworks in supporting enterprise AI initiatives. These innovations suggest a progressive shift in how organizations can leverage AI technology to drive informational and operational efficiency securely.