Over the last 18 months, AWS has announced more than twice as many machine learning (ML) and generative artificial intelligence (AI) features into general availability than 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 such as NASDAQ and United Airlines, to 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 empowers 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 evaluation. These services play a pivotal role in addressing diverse customer needs across the generative AI journey.
Amazon SageMaker, the foundational service for ML and generative AI model development, provides the fine-tuning and flexibility that makes it simple for data scientists and machine learning engineers to build, train, and deploy machine learning and foundation models (FMs) at scale. For application developers, Amazon Bedrock is the simplest way to build and scale generative AI applications with FMs 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 the 2024 Gartner Data Science and Machine Learning (DSML) Magic Quadrant reflects our commitment to meeting evolving customer needs, particularly in data science and ML. In our opinion, this recognition, coupled with our 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.
Review the Gartner Magic Quadrant and Methodology
For Gartner, the DSML Magic Quadrant research methodology provides a graphical competitive positioning of four types of technology providers in fast-growing markets: Leaders, Visionaries, Niche Players and Challengers. As companion research, Gartner Critical Capabilities notes provide deeper insight into the capability and suitability of providers’ IT products and services based on specific or customized use cases.
The following figure highlights where AWS lands in the DSML Magic Quadrant.
Access a complimentary copy of the full report to see why Gartner positioned AWS as a Leader, and dive deep into the strengths and cautions of AWS.
Further detail on Amazon Bedrock and Amazon SageMaker
Amazon Bedrock provides a straightforward way to build and scale applications with large language models (LLMs) and foundation models (FMs), empowering you to build generative AI applications with security and privacy. With Amazon Bedrock, you can experiment with and evaluate high performing FMs for your use case, import custom models, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that run tasks using your enterprise 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 broad set of tools to enable high-performance, low-cost ML for any use case. You can access a wide-ranging choices of ML tools, fully managed and scalable infrastructure, repeatable and responsible ML workflows and the power of human feedback across the ML lifecycle, including sophisticated tools that make it straightforward to work with data like Amazon SageMaker Canvas and Amazon SageMaker Data Wrangler.
In addition, Amazon SageMaker helps data scientists and ML engineers build FMs from scratch, evaluate and customize FMs with advanced techniques, and deploy FMs with fine-grained controls for generative AI use cases that have stringent requirements on accuracy, latency, and cost. Hundreds of thousands of customers from Perplexity to Thomson Reuters to Workday use SageMaker to build, train, and deploy ML models, including LLMs 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, 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 AWS.
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About the author
Susanne Seitinger leads AI and ML product marketing at Amazon Web Services (AWS), including the introduction of critical generative AI services like Amazon Bedrock as well as coordinating generative AI marketing activities across AWS. Prior to AWS, Susanne was the director of public sector marketing at Verizon Business Group, and previously drove public sector marketing in the United States for Signify, after holding various positions in R&D, innovation, and segment management and marketing. She holds a BA from Princeton University, as well as a master’s in city planning and a PhD from MIT.