{"id":34833,"date":"2024-06-25T13:00:00","date_gmt":"2024-06-25T20:00:00","guid":{"rendered":"https:\/\/azure.microsoft.com\/en-us\/blog\/?p=34833"},"modified":"2024-07-08T14:21:28","modified_gmt":"2024-07-08T21:21:28","slug":"microsoft-is-a-leader-in-the-2024-gartner-magic-quadrant-for-data-science-and-machine-learning-platforms","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/microsoft-is-a-leader-in-the-2024-gartner-magic-quadrant-for-data-science-and-machine-learning-platforms\/","title":{"rendered":"Microsoft is a Leader in the 2024 Gartner\u00ae Magic Quadrant\u2122 for Data Science and Machine Learning Platforms\u00a0"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Microsoft is a Leader in this year\u2019s Gartner\u00ae Magic Quadrant\u2122 for Data Science and Machine Learning Platforms. <a href=\"https:\/\/azure.microsoft.com\/en-us\/solutions\/ai\/\">Azure AI<\/a> provides a powerful, flexible end-to-end platform for accelerating data science and machine learning innovation while providing the enterprise governance that every organization needs in the era of AI.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"900\" height=\"995\" src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2024\/06\/MQ-Data-Science-and-Machine-Learning.webp\" alt=\"Magic Quadrant for Data Science and Maching Learning Platforms showing Gartner results for as of April 2024. \" class=\"wp-image-35074\" srcset=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2024\/06\/MQ-Data-Science-and-Machine-Learning.webp 900w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2024\/06\/MQ-Data-Science-and-Machine-Learning-271x300.webp 271w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2024\/06\/MQ-Data-Science-and-Machine-Learning-768x849.webp 768w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">In May 2024, Microsoft was also named a Leader for the fifth year in a row in the <a href=\"https:\/\/azure.microsoft.com\/en-us\/blog\/microsoft-is-a-leader-in-the-2024-gartner-magic-quadrant-for-cloud-ai-developer-services\/\">Gartner\u00ae Magic Quadrant\u2122 for Cloud AI Developer Services<\/a>, where we placed furthest for our Completeness of Vision. We\u2019re pleased by these recognitions from Gartner as we continue helping customers, from large enterprises to agile startups, bring their AI and machine learning models and applications into production securely and at scale.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Azure AI is at the forefront of purpose-built AI infrastructure, responsible AI tooling, and helping cross-functional teams collaborate effectively using Machine Learning Operations (MLOps) for generative AI and traditional machine learning projects. <a href=\"https:\/\/azure.microsoft.com\/en-us\/products\/machine-learning\/\">Azure Machine Learning<\/a> provides access to a broad selection of foundation models in the Azure AI model catalog\u2014including the recent releases of <a href=\"https:\/\/azure.microsoft.com\/en-us\/blog\/introducing-phi-3-redefining-whats-possible-with-slms\/\">Phi-3<\/a>, <a href=\"https:\/\/techcommunity.microsoft.com\/t5\/ai-ai-platform-blog\/introducing-jais-arabic-centric-large-language-model-on-azure\/ba-p\/4137329\" target=\"_blank\" rel=\"noreferrer noopener\">JAIS<\/a>, and <a href=\"https:\/\/azure.microsoft.com\/en-us\/blog\/introducing-gpt-4o-openais-new-flagship-multimodal-model-now-in-preview-on-azure\/\">GPT-4o<\/a>\u2014and tools to fine-tune or build your own machine learning models. Additionally, the platform supports a rich library of open-source frameworks, tools, and algorithms so that data science and machine learning teams can innovate in their own way, all on a trusted foundation.&nbsp;<\/p>\n\n\n\n<aside class=\"cta-block cta-block--align-left cta-block--has-image wp-block-msx-cta\" data-bi-an=\"CTA Block\">\n\t<div class=\"cta-block__content\">\n\t\t\t\t\t<div class=\"cta-block__image-container\">\n\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"575\" src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2024\/06\/Azure_Hero_Ellipse_LightGreen_GreenGrad-1024x575.webp\" class=\"cta-block__image\" alt=\"diagram, venn diagram\" srcset=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2024\/06\/Azure_Hero_Ellipse_LightGreen_GreenGrad-1024x575.webp 1024w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2024\/06\/Azure_Hero_Ellipse_LightGreen_GreenGrad-300x169.webp 300w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2024\/06\/Azure_Hero_Ellipse_LightGreen_GreenGrad-768x432.webp 768w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2024\/06\/Azure_Hero_Ellipse_LightGreen_GreenGrad.webp 1260w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/>\t\t\t<\/div>\n\t\t\n\t\t<div class=\"cta-block__body\">\n\t\t\t<h2 class=\"cta-block__headline\">Azure AI<\/h2>\n\t\t\t<p class=\"cta-block__text\">Microsoft is named a Leader in the 2024 Gartner\u00ae Magic Quadrant\u2122 for Data Science and Machine Learning Platforms\u00a0<\/p>\n\t\t\t\t\t\t\t<div class=\"cta-block__actions\">\n\t\t\t\t\t<a\n\t\t\t\t\t\thref=\"https:\/\/aka.ms\/2024-GartnerMQ-DSML\"\n\t\t\t\t\t\tclass=\"btn cta-block__link btn-light\"\n\t\t\t\t\t\ttarget=\"_blank\"\t\t\t\t\t>\n\t\t\t\t\t\tRead the report\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t<\/div>\n<\/aside>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"accelerate-time-to-value-with-azure-ai-infrastructure\">Accelerate time to value with Azure AI infrastructure<\/h2>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\" id=\"block-1ecfb40b-8a45-4194-87b6-032f9a254dda\">\u201c<em>We\u2019re now able to get a functioning model with relevant insights up and running in just a couple of weeks thanks to Azure Machine Learning. We\u2019ve even managed to produce verified models in just four to six weeks<\/em>.\u201d<\/p>\n<cite>Dr. Nico Wintergerst, Staff AI Research Engineer at <a href=\"https:\/\/customers.microsoft.com\/en-us\/story\/1540679886952231963-relayr-azure-iot-en\" target=\"_blank\" rel=\"noreferrer noopener\">relayr GmbH<\/a><\/cite><\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">Azure Machine Learning helps organizations build, deploy, and manage high-quality AI solutions quickly and efficiently, whether building large models from scratch, running inference on pre-trained models, consuming models as a service, or fine-tuning models for specific domains. Azure Machine Learning runs on the same <a href=\"https:\/\/www.youtube.com\/watch?v=DlX3QVFUtQI\" target=\"_blank\" rel=\"noreferrer noopener\">powerful AI infrastructure<\/a> that powers some of the world\u2019s most popular AI services, such as ChatGPT, Bing, and <a href=\"https:\/\/azure.microsoft.com\/en-us\/products\/ai-services\/openai-service\">Azure OpenAI Service<\/a>. Additionally, Azure Machine Learning\u2019s compatibility with ONNX Runtime and DeepSpeed can help customers further optimize training and inference time for performance, scalability, and power efficiency.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Whether your organization is training a deep learning model from scratch using open source frameworks or bringing an existing model into the cloud, Azure Machine Learning enables data science teams to scale out training jobs using elastic cloud compute resources and seamlessly transition from training to deployment. With <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/how-to-deploy-online-endpoints?view=azureml-api-2&amp;tabs=cli\" target=\"_blank\" rel=\"noreferrer noopener\">managed online endpoints<\/a>, customers can deploy models across powerful CPU and graphics processing unit (GPU) machines without needing to manage the underlying infrastructure\u2014saving time and effort. Similarly, customers do not need to provision or manage infrastructure when <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/concept-model-catalog?view=azureml-api-2#deployment-options\" target=\"_blank\" rel=\"noreferrer noopener\">deploying foundation models as a service<\/a> from the Azure AI model catalog. This means customers can easily deploy and manage thousands of models across production environments\u2014from on-premises to the edge\u2014for batch and real-time predictions. &nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"streamline-operations-with-flexible-mlops-and-llmops\">Streamline operations with flexible MLOps and LLMOps&nbsp;<\/h2>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\" id=\"block-dc3038f7-b79e-4af1-8d81-5f36b56aa253\">\u201c<em>Prompt flow helped streamline our development and testing cycles, which established the groundedness we required for making sure the customer and the solution were interacting in a realistic way<\/em>.\u201d<\/p>\n<cite>Fabon Dzogang, Senior Machine Learning Scientist at <a href=\"https:\/\/customers.microsoft.com\/en-us\/story\/1731404546482708710-asos-retailer-azure-ai-studio\" target=\"_blank\" rel=\"noreferrer noopener\">ASOS<\/a><\/cite><\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/concept-model-management-and-deployment?view=azureml-api-2\" target=\"_blank\" rel=\"noreferrer noopener\">Machine learning operations<\/a> (MLOps) and <a href=\"https:\/\/azure.microsoft.com\/en-us\/blog\/the-new-ai-imperative-unlock-repeatable-value-for-your-organization-with-llmops\/\">large language model operations<\/a> (LLMOps) sit at the intersection of people, processes, and platforms. As data science projects scale and applications become more complex, effective automation and collaboration tools become essential for achieving high-quality, repeatable outcomes.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Azure Machine Learning is a flexible MLOps platform, built to support data science teams of any size. The platform makes it easy for teams to share and govern machine learning assets, build repeatable pipelines using built-in interoperability with Azure DevOps and GitHub Actions, and continuously monitor model performance in production. Data connectors with Microsoft sources such as <a href=\"https:\/\/techcommunity.microsoft.com\/t5\/ai-machine-learning-blog\/avail-the-power-of-microsoft-fabric-from-within-azure-machine\/ba-p\/3980702\" target=\"_blank\" rel=\"noreferrer noopener\">Microsoft Fabric<\/a> and <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/how-to-connection?view=azureml-api-2&amp;tabs=cli\" target=\"_blank\" rel=\"noreferrer noopener\">external sources<\/a> such as Snowflake and Amazon S3, further simplify MLOps. <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/concept-mlflow?view=azureml-api-2\" target=\"_blank\" rel=\"noreferrer noopener\">Interoperability with MLflow<\/a> also makes it seamless for data scientists to scale existing workloads from local execution to the cloud and edge, while storing all MLflow experiments, run metrics, parameters, and model artifacts in a centralized workspace.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/prompt-flow\/overview-what-is-prompt-flow\" target=\"_blank\" rel=\"noreferrer noopener\">Azure Machine Learning prompt flow<\/a> helps streamline the entire development cycle for generative AI applications with its LLMOps capabilities, orchestrating executable flows comprised of models, prompts, APIs, Python code, and tools for vector database lookup and content filtering. Azure AI prompt flow can be used together with popular open-source frameworks like LangChain and Semantic Kernel, enabling developers to bring experimental flows into prompt flow to scale those experiments and run comprehensive evaluations. Developers can debug, share, and iterate on applications collaboratively, integrating built-in testing, tracing, and evaluation tools <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/prompt-flow\/how-to-integrate-with-llm-app-devops?view=azureml-api-2&amp;tabs=cli\" target=\"_blank\" rel=\"noreferrer noopener\">into their CI\/CD system<\/a> to continually reassess the quality and safety of their application. Then, developers can deploy applications when ready with one click and <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/prompt-flow\/how-to-monitor-generative-ai-applications?view=azureml-api-2\" target=\"_blank\" rel=\"noreferrer noopener\">monitor flows<\/a> for key metrics such as latency, token usage, and generation quality in production. The result is end-to-end observability and continuous improvement.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"develop-more-trustworthy-models-and-apps\">Develop more trustworthy models and apps&nbsp;<\/h2>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\">\u201c<em>The responsible AI dashboard provides valuable insights into the performance and behavior of computer vision models, providing a better level of understanding into why some models perform differently than others, and insights into how various underlying algorithms or parameters influence performance. The benefit is better-performing models, enabled and optimized with less time and effort<\/em>.\u201d&nbsp;<\/p>\n<cite>\u2014Teague Maxfield, Senior Manager at <a href=\"https:\/\/customers.microsoft.com\/en-us\/story\/1548724923828850434-constellation-clearsight-energy-azure-machine-learning\" target=\"_blank\" rel=\"noreferrer noopener\">Constellation Clearsight<\/a>&nbsp;<\/cite><\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">AI principles such as fairness, safety, and transparency are not self-executing. That\u2019s why Azure Machine Learning provides data scientists and developers with practical tools to operationalize responsible AI right in their flow of work, whether they need to assess and debug a traditional machine learning model for bias, protect a foundation model from prompt injection attacks, or monitor model accuracy, quality, and safety in production.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/concept-responsible-ai-dashboard?view=azureml-api-2\" target=\"_blank\" rel=\"noreferrer noopener\">Responsible AI dashboard<\/a> helps data scientists assess and debug traditional machine learning models for fairness, accuracy, and explainability throughout the machine learning lifecycle. Users can also generate a <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/concept-responsible-ai-scorecard?view=azureml-api-2\" target=\"_blank\" rel=\"noreferrer noopener\">Responsible AI scorecard<\/a> to document and share model performance details with business stakeholders, for more informed decision-making. Similarly, developers in Azure Machine Learning can review model cards and benchmarks and perform their own evaluations to select the best foundation model for their use case from the <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/concept-model-catalog?view=azureml-api-2#model-catalog-capabilities-overview\" target=\"_blank\" rel=\"noreferrer noopener\">Azure AI model catalog<\/a>. Then they can apply a defense-in-depth approach to mitigating AI risks using built-in capabilities for <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/concept-model-catalog?view=azureml-api-2#content-safety-for-models-deployed-with-managed-compute\" target=\"_blank\" rel=\"noreferrer noopener\">content filtering<\/a>, <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/concept-retrieval-augmented-generation?view=azureml-api-2\" target=\"_blank\" rel=\"noreferrer noopener\">grounding on fresh data<\/a>, and prompt engineering with <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/ai-services\/openai\/concepts\/system-message\" target=\"_blank\" rel=\"noreferrer noopener\">safety system messages<\/a>. <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/prompt-flow\/how-to-bulk-test-evaluate-flow?view=azureml-api-2\" target=\"_blank\" rel=\"noreferrer noopener\">Evaluation tools<\/a> in prompt flow enable developers to iteratively measure, improve, and document the impact of their mitigations at scale, using built-in metrics and custom metrics. That way, data science teams can deploy solutions with confidence while providing transparency for business stakeholders.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Read more on <a href=\"https:\/\/azure.microsoft.com\/en-us\/solutions\/ai\/responsible-ai-with-azure\/\" target=\"_blank\" rel=\"noreferrer noopener\">Responsible AI with Azure<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"deliver-enterprise-security-privacy-and-compliance\">Deliver enterprise security, privacy, and compliance&nbsp;<\/h2>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\">\u201c<em>We needed to choose a platform that provided best-in-class security and compliance due to the sensitive data we require and one that also offered best-in-class services as we didn&#8217;t want to be an infrastructure hosting company. We chose Azure because of its scalability, security, and the immense support it offers in terms of infrastructure management<\/em>.\u201d<\/p>\n<cite>\u2014Michael Calvin, Chief Technical Officer at <a href=\"https:\/\/customers.microsoft.com\/en-us\/story\/1769930620896500264-kinectify-azure-openai-service-other-en-united-states\" target=\"_blank\" rel=\"noreferrer noopener\">Kinectify<\/a><\/cite><\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">In today&#8217;s data-driven world, effective data security, governance, and privacy require every organization to have a comprehensive understanding of their data and AI and machine learning systems. AI governance also requires effective collaboration between diverse stakeholders, such as IT administrators, AI and machine learning engineers, data scientists, and risk and compliance roles. In addition to enabling enterprise observability through MLOps and LLMOps, Azure Machine Learning helps organizations ensure that data and models are protected and compliant with the highest standards of security and privacy. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">With Azure Machine Learning, IT administrators can restrict access to resources and operations by user account or groups, control incoming and outgoing network communications, encrypt data both in transit and at rest, scan for vulnerabilities, and centrally manage and audit configuration policies through <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/governance\/policy\/?view=azureml-api-2\" target=\"_blank\" rel=\"noreferrer noopener\">Azure Policy<\/a>. Data governance teams can also connect Azure Machine Learning to <a href=\"https:\/\/learn.microsoft.com\/en-us\/purview\/register-scan-azure-machine-learning\" target=\"_blank\" rel=\"noreferrer noopener\">Microsoft Purview<\/a>, so that metadata on AI assets\u2014including models, datasets, and jobs\u2014is automatically published to the Microsoft Purview Data Map. This enables data scientists and data engineers to observe how components are shared and reused and examine the lineage and transformations of training data to understand the impact of any issues in dependencies. Likewise, risk and compliance professionals can track what data is used to train models, how base models are fine-tuned or extended, and where models are employed across different production applications, and use this as evidence in compliance reports and audits.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Lastly, with the <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/how-to-attach-kubernetes-anywhere?view=azureml-api-2\" target=\"_blank\" rel=\"noreferrer noopener\">Azure Machine Learning Kubernetes extension<\/a> enabled by Azure Arc, organizations can run machine learning workloads on any Kubernetes clusters, ensuring data residency, security, and privacy compliance across hybrid public clouds and on-premises environments. This allows organizations to process data where it resides, meeting stringent regulatory requirements while maintaining flexibility and control over their MLOps. Customers using <a href=\"https:\/\/github.com\/Azure-Samples\/azure-ml-federated-learning\" target=\"_blank\" rel=\"noreferrer noopener\">federated learning techniques<\/a> along with Azure Machine Learning and <a href=\"https:\/\/azure.microsoft.com\/en-us\/solutions\/confidential-compute\/\" target=\"_blank\" rel=\"noreferrer noopener\">Azure confidential computing<\/a> can also train powerful models on disparate data sources, all without copying or moving data from secure locations.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"get-started-with-azure-machine-learning\">Get started with Azure Machine Learning&nbsp;<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Machine learning continues to transform the way businesses operate and compete in the digital era\u2014whether you want to optimize your business operations, enhance customer experiences, or innovate.&nbsp;<a href=\"https:\/\/azure.microsoft.com\/en-us\/products\/machine-learning\/\">Azure Machine Learning<\/a> provides a powerful, flexible machine learning and data science platform to operationalize AI innovation responsibly. &nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li class=\"wp-block-list-item\">Read the <a href=\"https:\/\/aka.ms\/2024-GartnerMQ-DSML\" target=\"_blank\" rel=\"noreferrer noopener\">2024 Gartner\u00ae Magic Quadrant\u2122 for Data Science and Machine Learning Platforms<\/a> report.<\/li>\n\n\n\n<li class=\"wp-block-list-item\">Learn more about Microsoft\u2019s placement in the blog post &#8220;<a href=\"https:\/\/azure.microsoft.com\/en-us\/blog\/microsoft-is-a-leader-in-the-2024-gartner-magic-quadrant-for-cloud-ai-developer-services\/\">Gartner\u00ae Magic Quadrant\u2122 for Cloud AI Developer Services<\/a>.&#8221;<\/li>\n\n\n\n<li class=\"wp-block-list-item\">Explore more on the <a href=\"https:\/\/customers.microsoft.com\/en-us\/search?sq=%22Azure%20Machine%20Learning%22&amp;ff=&amp;p=0&amp;so=story_publish_date%20desc\" target=\"_blank\" rel=\"noreferrer noopener\">Microsoft Customer Stories blog<\/a>.&nbsp;<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p class=\"wp-block-paragraph\">*Gartner, Magic Quadrant for Data Science and Machine Learning Platforms, By Afraz Jaffri, Aura Popa, Peter Krensky, Jim Hare, Raghvender Bhati, Maryam Hassanlou, Tong Zhang, 17 June 2024.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Gartner, Magic Quadrant for Cloud AI Developer Services, Jim Scheibmeir, Arun Batchu, Mike Fang, Published 29 April 2024.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">GARTNER is a registered trademark and service mark of Gartner, Inc. and\/or its affiliates in the U.S. and internationally, Magic Quadrant is a registered trademark of Gartner, Inc. and\/or its affiliates and is used herein with permission. All rights reserved.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">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\u2019s Research &amp; Advisory 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.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">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 <a href=\"https:\/\/aka.ms\/2024-GartnerMQ-DSML\" target=\"_blank\" rel=\"noreferrer noopener\">this link.<\/a>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Microsoft is a Leader in this year\u2019s Gartner\u00ae Magic Quadrant\u2122 for Data Science and Machine Learning Platforms. Azure AI provides a powerful, flexible end-to-end platform for accelerating data science and machine learning innovation.<\/p>\n","protected":false},"author":61,"featured_media":34834,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"ms_queue_id":[],"ep_exclude_from_search":false,"_classifai_error":"","_classifai_text_to_speech_error":"","_alt_title":"","footnotes":"","msx_community_cta_settings":[]},"categories":[1454,1485],"tags":[2671,1790,2769],"audience":[],"content-type":[1465],"product":[1803,1493],"tech-community":[],"topic":[],"coauthors":[2746],"class_list":["post-34833","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-machine-learning","category-internet-of-things","tag-ai","tag-gartner-magic-quadrant","tag-llmops","content-type-announcements","product-azure-ai","product-azure-machine-learning","review-flag-1680286581-295","review-flag-1680286581-56","review-flag-3-1680286581-173","review-flag-and-o-1680286581-349","review-flag-gartn-1680286585-730","review-flag-machi-1680286585-314","review-flag-microsofts"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Microsoft is a Leader in the 2024 Gartner\u00ae Magic Quadrant\u2122 for Data Science and Machine Learning Platforms\u00a0 | Microsoft Azure Blog<\/title>\n<meta name=\"description\" content=\"Microsoft is a Leader in this year\u2019s Gartner\u00ae Magic Quadrant\u2122 for Data Science and Machine Learning Platforms. 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