In 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 all 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, to realize the transformative potential of generated AI. It is possible to release it. AWS provides a secure, high-performance, and scalable set of data science and machine learning services and capabilities that enable businesses to drive innovation through the power of AI.
At the heart of this innovation are Amazon Bedrock and Amazon SageMaker, both of which were mentioned in the recent Gartner Data Science and Machine Learning (DSML) Magic Quadrant review. These services play a critical role in addressing the diverse needs of customers across their generative AI efforts.
Amazon SageMaker, the foundational service for ML and generative AI model development, provides fine-tuning and flexibility that makes it easy for data scientists and machine learning engineers to build, train, and deploy machine learning and foundational models (FMs) at scale. We provide. For application developers, Amazon Bedrock is the easiest way to build and scale generative AI applications using FM for a variety of use cases. Whether you want to leverage the best of FM or import custom models from SageMaker, Bedrock gives development teams the tools they need to accelerate innovation.
We believe that continued innovation in both services and our position as a leader in the 2024 Gartner Data Science and Machine Learning (DSML) Magic Quadrant reflects our commitment to meeting evolving customer needs, especially in data science and ML. I believe it reflects that. In our opinion, this recognition, coupled with its recent recognition in the Cloud AI Developer Services (CAIDS) Magic Quadrant, solidifies AWS as a provider of innovative AI solutions that drive business value and competitive advantage. I think it’s something you can make into something.
Explore Gartner’s Magic Quadrant and Methodology
For Gartner, the DSML Magic Quadrant research methodology provides a graphical view of the competitive positioning of four types of technology providers (Leaders, Visionaries, Niche Players, and Challengers) in fast-growing markets. As a related study, Gartner Critical Capabilities notes provide deeper insight into the capabilities and suitability of a provider’s IT products and services based on specific or customized use cases.
The following diagram shows where AWS sits in the DSML Magic Quadrant.
Access your free copy of the full report to see why Gartner positions AWS as a leader and dive deeper into AWS’s strengths and caveats.
Learn more about Amazon Bedrock and Amazon SageMaker
Amazon Bedrock provides an easy way to build and extend applications using large-scale language models (LLMs) and foundational models (FMs), enabling 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, and make your models private with your data using techniques such as fine-tuning and acquisition augmentation generation (RAG). You can customize and build agents to perform tasks using: Enterprise systems and data sources. Tens of thousands of customers across multiple industries are deploying new generative AI experiences for a variety of use cases.
Amazon SageMaker is a fully managed service that brings together a broad set of tools to enable high-performance, low-cost ML for any use case. Amazon SageMaker Canvas and Amazon SageMaker Data Wrangler.
Additionally, Amazon SageMaker enables data scientists and ML engineers to build FMs from scratch, evaluate and customize FMs with advanced techniques, and provides fine-grained control for generative AI use cases with stringent requirements for accuracy, latency, and cost. FM. Hundreds of thousands of customers, from Perplexity to Thomson Reuters to Workday, use SageMaker to build, train, and deploy ML models, including LLM and other FMs.
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, express 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. Gartner documentation 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 its affiliates in the United States and other countries and is used herein with permission. Unauthorized reproduction is prohibited.
About the author
Suzanne Zeitinger leads AI and ML product marketing at Amazon Web Services (AWS), including introducing key generative AI services such as Amazon Bedrock and coordinating generative AI marketing efforts across AWS. Prior to joining AWS, he was director of public sector marketing at Verizon Business Group, where he held various roles in research and development, innovation, segment management and marketing, and drove U.S. public sector marketing at Signify. . She holds a bachelor’s degree from Princeton University and a master’s degree and doctorate in urban planning from MIT.
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