Over the past 18 months, AWS has announced the general availability of more than twice as many machine learning (ML) and generative artificial intelligence (ai) capabilities as the other major cloud providers combined. This accelerated innovation is enabling organizations of all sizes—from disruptive ai startups like Hugging Face, AI21 Labs, and Articul8 ai to industry leaders like NASDAQ and United Airlines—unlock the transformative potential of generative ai. By providing a secure, high-performance, and scalable set of data science and machine learning services and capabilities, AWS enables businesses to drive innovation through the power of ai.
At the heart of this innovation are amazon Bedrock and amazon SageMaker, both mentioned in Gartner's recent Data Science and Machine Learning (DSML) Magic Quadrant assessment. These services play a critical role in addressing diverse customer needs along the generative ai journey.
amazon SageMaker, the foundational service for developing machine learning and generative ai models, provides the tuning and flexibility that makes it easy for data scientists and machine learning engineers to build, train, and deploy basic and machine learning models. (FM) to scale. For application developers, amazon Bedrock is the easiest way to build and scale generative ai applications with FM for a wide variety of use cases. Whether leveraging the best FMs out there or importing custom models from SageMaker, Bedrock equips development teams with the tools they need to accelerate innovation.
We believe continued innovations for both services and our positioning as a leader in Gartner's 2024 Data Science and Machine Learning (DSML) Magic Quadrant reflect our commitment to meeting customers' evolving needs, particularly in data science and ML. . In our view, this recognition, along with our recent recognition in the Magic Quadrant for Cloud ai Developer Services (CAIDS), solidifies AWS as a provider of innovative ai solutions that drive business value and competitive advantage.
Review the Magic Quadrant and Gartner Methodology
For Gartner, the DSML Magic Quadrant research methodology provides graphical competitive positioning of four types of technology providers in fast-growing markets: leaders, visionaries, niche players, and challengers. As complementary research, Gartner Critical Capabilities notes provide deeper insight into the capability and suitability of vendors' IT products and services based on specific or customized use cases.
The following figure highlights where AWS sits in the DSML Magic Quadrant.
Access a free copy Read the full report to see why Gartner positioned AWS as a leader and delve into AWS's strengths and cautions.
More details about amazon Bedrock and amazon SageMaker
amazon Bedrock provides an easy way to build and scale applications with large language models (LLM) and core models (FM), allowing you to build generative ai applications with security and privacy. With amazon Bedrock, you can experiment and evaluate high-performance FM for your use case, import custom models, privately customize them with your data using techniques such as fine-tuning and recovery augmented generation (RAG), and create agents that run tasks using your business systems and data sources. Tens of thousands of customers across multiple industries are deploying new generative ai experiences for diverse use cases.
amazon SageMaker is a fully managed service that brings together a rich set of tools to enable high-performance, low-cost ML for any use case. You can access a wide range of machine learning tool options, a fully managed and scalable infrastructure, repeatable and accountable machine learning workflows, and the power of human feedback throughout the machine learning lifecycle, including sophisticated tools that make it easier to work with data such as amazon SageMaker Canvas and amazon SageMaker Data Controller.
Additionally, amazon SageMaker helps data scientists and machine learning engineers build FM from scratch, evaluate and customize FM with advanced techniques, and deploy FM with fine-grained controls for generative ai use cases that have strict requirements for accuracy, latency and cost. Hundreds of thousands of customers, from Perplexity to Thomson Reuters and Workday, use SageMaker to build, train, and deploy machine learning models, including LLM and other FMs.
Gartner does not endorse any vendor, product or service described in its research publications and does not recommend technology users 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, express or implied, with respect to this research, including any warranty of merchantability or fitness for a particular purpose.
This chart 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 AWS.
GARTNER is a registered trademark and service mark of Gartner and Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.
About the author
Susanne Seitinger leads ai and ML product marketing on amazon Web Services (AWS), including the introduction of critical generative ai services such as amazon Bedrock, as well as coordinating generative ai marketing activities on AWS. Prior to AWS, Susanne was Director of Public Sector Marketing at Verizon Business Group and previously led public sector marketing in the United States for Signify, after holding various roles in R&D, innovation, and segment management and marketing. He has a bachelor's degree from Princeton University, as well as a master's degree in urban planning and a doctorate from MIT.
