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Google is a Leader in the 2024 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms

Today, we are excited to announce that Gartner® has named Google as a Leader in the Magic Quadrant™ for Data Science and Machine Learning Platforms.

Download the complimentary 2024 Gartner Magic Quadrant™ for Data Science and Machine Learning Platforms.

We believe that our recognition stems from the fact that Google is uniquely positioned to address the needs of customers and their data science and machine learning workloads.

It starts with our pioneering AI research and development. We’ve invented some of the most important AI technologies fueling the current revolution, like transformers, compute-optimal training, optical circuit switch networks, and even application-specific chips such as the Tensor Processing Unit. Our research has earned over 2.6 billion citations and counting.

We have proven our expertise in taking AI to production with global scale, as demonstrated by 20+ years of experience integrating AI innovations into large-scale applications, such as YouTube, Maps, Search, Ads, Workspace, Photos, and more. AI is in our DNA.

All of this experience is funneled into Vertex AI, our unified AI platform that provides leading AI infrastructure, state-of-the-art models and managed services built for data scientists and their collaborators. With capabilities across both predictive and generative AI, Vertex AI helps customers unlock AI-powered digital transformation across the enterprise.

Unified AI Platform

Vertex AI provides a truly unified predictive and generative AI experience for any Data Science and Machine Learning (DSML) team. On one collaborative platform DSML practitioners can build, deploy, and manage any type of AI/ML model. Whether starting from scratch, leveraging low-code model building tools like AutoML, or customizing existing foundational models, practitioners can easily leverage data and AI tooling, utilize state of the art techniques for tuning and augmenting models, and access a robust set of MLOps tools.

A unified interface for data analysis and AI development helps kickstart AI-powered projects across various tasks and modalities. Colab Enterprise offers a managed service that combines the ease-of-use of Google’s Colab notebooks with enterprise-level security and compliance support capabilities. With it, data scientists can work collaboratively to accelerate AI workflows with access to the full range of Vertex AI platform capabilities, integration with BigQuery for direct data access, and even code completion and generation.

Consistent workflows with minimal data movement provide simplified data governance. Along with Colab Enterprise, Vertex AI Feature Store is built on BigQuery to help avoid data duplication and preserve data access policies.

Customization tooling via Model Builder enables practitioners to work with existing foundation models to create differentiated AI capabilities with enterprise data. A range of capabilities make it easy for practitioners of all skill levels to customize models:

Prompt design, which lets practitioners give the model instructionsSupervised tuning, including adapter-based and Low-Rank Adaptation (LoRA), which allows practitioners to customize the model in an efficient, lower-cost wayReinforcement Learning from Human Feedback (RLHF), which helps align model outputs with human valuesDistillation, which transfer knowledge from a larger model to a smaller modelFor more advanced users, Vertex AI Training and Prediction allow DSML experts to build models from scratch and deploy them by leveraging state-of-the-art infrastructure

Augmentation tooling via Agent Builder adapts to the practitioner’s workflow, offering no-code, low-code, and code-first solutions for streamlined AI-powered agent development. These are augmented by comprehensive tools for orchestration, out-of-the-box grounding and DIY Retrieval RAG components, function calling and connectors, and data augmentation. In particular, DSML practitioners have the option of grounding their model outputs in Google Search and their enterprise’s data to combine the power of Google’s latest foundation models with access to fresh, high-quality information, which can significantly improve completeness and accuracy of responses.

MLOps tooling for predictive and generative AI ensures AI projects can reach production and deliver real business value. Teams can collaborate to improve models throughout the entire development life cycle—whether it’s to identify the best model for a use case with Vertex AI Evaluation, orchestrate workflows with Vertex AI Pipelines, manage models Model Registry, serve, share, and reuse ML features with Feature Store, or monitor models for input skew and drift. And with the evolution of generative AI, we’re continually adding new capabilities:

Prompt assistance and management to version prompts and track lineage, share notes, run side by side comparisons, and even use AI assisted prompting.Rapid evaluation (in preview) allows practitioners to work across a larger set of models to quickly evaluate model performance in seconds based on a small data set.Automatic side by side, now GA, uses one large language model to evaluate two other models, and provides explanations and certainty scores to help you evaluate models at scale.

World-class AI Infrastructure

With Vertex AI, DSML practitioners can deliver results faster with purpose-built, managed infrastructure.

This includes a wide variety of hardware options, including both TPUs and GPUs for fast and cost effective training, tuning, and serving. With this, practitioners can:

Speed up training and inference time with high-performance computingScale AI models exponentiallyLeverage our fully-managed AI platform optimized for efficiencyBuild with an open source software ecosystem

Best-in-class Models, Enterprise-ready

Vertex AI accelerates time to value by providing customers with a curated selection of 150+ pre-trained and foundation models in Vertex AI Model Garden, fully integrated with an end to end AI platform. Models with multimodal reasoning capabilities enable rapid deployment across vision, language, conversation, and structured data. With different sizes available, customers can choose the right model for their preferred price, performance, and latency considerations. These models include:

Google models like Gemini, Imagen for text-to-image, Chirp for speech-to-text and moreDomain specific models such as MedLM and SecLMOpen and partner models including Google’s Gemma along with Anthropic’s Claude 3, EfficientNet, Meta’s Llama 3, Mistral 7B, TII’s Falcon, T-5 FLAN and ViT. For customers looking for even more variety, we offer an integration with Hugging Face that enables one-click model deployment from Hugging Face to Vertex AI.

Whether 1st-party, 3rd-party, or open-source, Google Cloud gives you the tools, services, and infrastructure to make every model enterprise ready.

Data governance and privacy means data and IP is protected while customizing foundation modelsSecurity and compliance support helps to enable AI use across the enterpriseResponsible AI tooling helps ensure effective and safe use of models

Choice and flexibility

Our software leadership directly empowers valuable products. We’ve created some of the most popular open-source software projects in the world, like TensorFlow, Kubernetes, Jax, and XLA. And we’re proud to say that since 2022, Google has been the largest contributor to open source, according to the Open Source Contributor Index. We will continue to invest in these diverse software initiatives and make them even better and well supported for enterprises.

Choice and flexibility is at the core of Google Cloud whether with data, DSML tools, models, or infra options. This flexibility is augmented by our key partnerships. We work closely with our partners across the stack to help our customers harness the power of AI, including partnerships with:

Foundation model creators and open source models providers, making these models available to customers through Vertex AI Model Garden.Leading tool providers for generative AI development, including Gretel, Labelbox and more, making Vertex AI the most comprehensive platform to build generative AI applications.Top SaaS providers to integrate AI into their solutions, including GitLab, Jasper, SAP, and more.Leading consulting firms and systems integrators to help enterprises adopt generative AI.

Leading AI companies choose Google Cloud

We are honored to have over 70% of the most innovative gen AI players in the world choose to build on Google Cloud. The companies that are the most sophisticated and knowledgeable in this space are choosing us because AI is in our DNA. This includes organizations like Anthropic, Cohere, Stability.ai, and Typeface.

What’s next

Google Cloud is committed to helping organizations build and deploy AI and we are investing heavily in bringing new predictive and generative AI capabilities to Vertex AI. In addition to our recognition in the 2024 Gartner Magic Quadrant™ for Data Science and Machine Learning Platforms, Gartner also recently named Google as a Leader in the 2024 Gartner® Magic Quadrant™ for Cloud AI Developer Services.

To download the full 2024 Gartner Magic Quadrant™ for Data Science and Machine Learning Platforms report, click here, and for more information on Vertex AI see here.

2024 Gartner Magic Quadrant for Data Science and Machine Learning Platforms – Afraz Jaffri, Aura Popa, Peter Krensky, Jim Hare, Raghvender Bhati, Maryam Hassanlou and Tong Zhang, June 17, 2024. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Google. Gartner and Magic Quadrant are registered trademarks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.

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