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The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
2,423
شركات
لدينا بيانات عن 2,423 شركات تستخدم Kubeflow. قائمة عملاء Kubeflow الخاصة بنا متاحة للتنزيل ومُزودة بمواصفات حيوية للشركات، بما في ذلك تصنيف الصناعة، حجم المنظمة، الموقع الجغرافي، جولات التمويل، وأرقام الإيرادات، وغيرها.
شركة | الدولة | الصناعة | الموظفون | الإيرادات |
---|---|---|---|---|
Tripadvisor | الولايات المتحدة | Software Development | 5.8K | $777M |
Zscaler | الولايات المتحدة | Computer And Network Security | 8.1K | $1.1B |
![]() Okta | الولايات المتحدة | Software Development | 7.7K | $1.3B |
![]() Rokt | الولايات المتحدة | Software Development | 585 | |
Bain & Company | الولايات المتحدة | Business Consulting And Services | 22K | $6B |
Canonical | المملكة المتحدة | Software Development | 1.5K | $126M |
Ayasdi | الولايات المتحدة | Software Development | 50 | $12M |
![]() Affirm | الولايات المتحدة | Financial Services | 2.6K | $1.2B |
![]() Wayfair | الولايات المتحدة | Retail | 14K | $12B |
![]() Seldon | المملكة المتحدة | Embedded Software Products | 110 | $11M |
UKG | الولايات المتحدة | Software Development | 15K | $1.5B |
Grupo Boticário | البرازيل | Wellness And Fitness Services | 29K | $1.4B |
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سجل الآن وحمل القائمة الكاملة بمعلومات 2,423 شركات
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إحصائيات استخدام التقنية وحصة السوق
يمكنك تخصيص هذه البيانات وفقًا لاحتياجاتك عن طريق التصفية للجغرافيا، الصناعة، حجم الشركة، الإيرادات، استخدام التكنولوجيا، الوظائف الشاغرة والمزيد. يمكنك تنزيل البيانات بتنسيق Excel أو CSV.
يمكنك الحصول على تنبيهات لهذه البيانات. يمكنك البدء عن طريق اختيار التكنولوجيا التي تهتم بها ومن ثم ستتلقى تنبيهات في صندوق الوارد الخاص بك عندما تكون هناك شركات جديدة تستخدم تلك التكنولوجيا.
يمكنك تصدير بياناته إلى ملف Excel، والذي يمكن استيراده إلى CRM الخاص بك. يمكنك أيضًا تصدير البيانات إلى API.
تُستخدم Kubeflow في 57 من البلدان
هناك 76 بدائل لـ Kubeflow
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الأسئلة المتكررة
يتم الحصول على بياناتنا من إعلانات الوظائف المجمعة من ملايين الشركات. نحن نراقب هذه الإعلانات على مواقع الشركات، ولوحات الوظائف، ومنصات التوظيف الأخرى. يوفر تحليل إعلانات الوظائف طريقة موثوقة لفهم التقنيات التي تستخدمها الشركات، بما في ذلك استخدامها للأدوات الداخلية.
نقوم بتحديث بياناتنا يوميًا لضمان وصولك إلى أحدث المعلومات المتاحة. تضمن هذه العملية المتكررة للتحديث أن تعكس رؤيتنا وذكاؤنا التطورات والاتجاهات الأخيرة في الصناعة.
Kubeflow is an open-source platform designed to make deploying, training, and managing machine learning models on Kubernetes easier. It aims to streamline the machine learning workflow by providing a unified toolset that integrates well with the Kubernetes ecosystem. With Kubeflow, data scientists and machine learning engineers can experiment, build, and deploy machine learning models more efficiently.
Kubeflow falls under the category of Machine Learning Tools, specifically focusing on infrastructure and operations for machine learning projects. It offers a set of components that work together to provide a seamless experience for running machine learning workflows on Kubernetes clusters. By leveraging the power of Kubernetes, Kubeflow enables scalable and reliable deployment of machine learning models in production environments.
Kubeflow was founded by Google in December 2017 with the motivation to simplify the implementation of machine learning workflows on Kubernetes. Google recognized the need for a more streamlined approach to handling machine learning pipelines and decided to create an open-source project that leverages the capabilities of Kubernetes for this purpose. Since its inception, Kubeflow has gained popularity in the machine learning community and has seen significant adoption by organizations looking to scale their machine learning operations.
Currently, Kubeflow holds a notable market share within the Machine Learning Tools category, with a growing user base. As the adoption of machine learning technologies continues to rise across industries, the demand for efficient machine learning infrastructure solutions like Kubeflow is expected to increase. With its ongoing development and community support, Kubeflow is forecasted to experience further growth in the market as more organizations embrace Kubernetes-based machine learning workflows.
Kubeflow is a powerful platform that companies use to streamline their machine learning workflows, from experimentation to production deployment. By leveraging the capabilities of Kubeflow, organizations can optimize their machine learning models and drive better decision-making processes.
Scalability and Flexibility: Kubeflow enables companies to scale their machine learning operations efficiently and adapt to changing business needs. Unlike traditional solutions, Kubeflow leverages Kubernetes' dynamic orchestration capabilities to allocate resources based on workload demands, ensuring optimal performance at all times.
End-to-End Machine Learning Lifecycle Management: With Kubeflow, companies can manage the entire machine learning lifecycle seamlessly within a single platform. From data preparation to model training and serving, Kubeflow simplifies the process by providing integrated tools and workflows, eliminating the need for multiple disjointed solutions.
Enhanced Collaboration and Reproducibility: Kubeflow promotes collaboration among data scientists and engineers by facilitating the sharing of models, datasets, and experiments in a reproducible manner. Unlike standalone tools, Kubeflow's version-controlled environments ensure that team members can work together effectively and track changes effortlessly.
Cost-Efficiency and Resource Optimization: By leveraging Kubeflow's resource management capabilities, companies can optimize their infrastructure usage and reduce operational costs. Kubeflow's ability to dynamically adjust resource allocation based on workload requirements ensures that organizations only pay for the resources they need, leading to significant cost savings in the long run.
Ecosystem Integration and Extensibility: Kubeflow seamlessly integrates with a wide range of existing machine learning frameworks and tools, enabling companies to leverage their preferred technologies within the platform. This extensibility allows organizations to build custom workflows and incorporate specialized libraries easily, enhancing their machine learning capabilities without constraints.
Kubeflow has become a popular choice for companies looking to streamline their machine learning workflows and enhance their AI capabilities. Here are some real-world case studies showcasing how various companies have successfully implemented Kubeflow to drive innovation and efficiency:
Airbnb: Airbnb, the online marketplace for vacation rentals, leverages Kubeflow to manage and scale their machine learning models efficiently. They started using Kubeflow in early 2019 to automate the deployment of ML models, enabling faster experimentation and iteration. By utilizing Kubeflow Pipelines, Airbnb's data science team can orchestrate complex workflows, leading to improved productivity and model accuracy.
Zynga: Zynga, a leading mobile game developer, incorporates Kubeflow into their AI infrastructure to enhance player experiences and optimize in-game mechanics. Since adopting Kubeflow in 2020, Zynga has seen remarkable improvements in model training speed and resource utilization. By utilizing Kubeflow's advanced hyperparameter tuning capabilities, Zynga can fine-tune their game algorithms quickly, resulting in more engaging gameplay for their users.
Cerner Corporation: As a healthcare technology company, Cerner Corporation utilizes Kubeflow to drive innovation in personalized patient care and clinical decision support systems. They integrated Kubeflow into their AI framework in 2018 to accelerate the development of predictive models for disease diagnosis and treatment planning. By leveraging Kubeflow's scalable infrastructure, Cerner has been able to deploy cutting-edge machine learning solutions that improve patient outcomes and streamline healthcare operations.
These case studies highlight the diverse applications of Kubeflow across different industries and emphasize its role in enabling companies to harness the power of machine learning effectively. By harnessing Kubeflow's capabilities, businesses can achieve greater agility, scalability, and accuracy in their AI initiatives, ultimately driving competitive advantage and business growth.
يمكنك الوصول إلى قائمة محدثة من الشركات التي تستخدم Kubeflow بزيارة TheirStack.com. توفر منصتنا قاعدة بيانات شاملة للشركات التي تستخدم تقنيات وأدوات داخلية متنوعة.
حتى الآن، لدينا بيانات حول 2,423 من الشركات التي تستخدم Kubeflow.
Kubeflow يُستخدم من قبل مجموعة متنوعة من المنظمات عبر مختلف الصناعات، بما في ذلك "Software Development", "Computer And Network Security", "Software Development", "Software Development", "Business Consulting And Services", "Software Development", "Software Development", "Financial Services", "Retail", "Embedded Software Products". للحصول على قائمة شاملة بجميع الصناعات التي تستخدم Kubeflow، يرجى زيارة TheirStack.com.
بعض الشركات التي تستخدم Kubeflow تشمل Tripadvisor, Zscaler, Okta, Rokt, Bain & Company, Canonical, Ayasdi, Affirm, Wayfair, Seldon والعديد غيرها. يمكنك العثور على قائمة كاملة بـ 2,423 شركات التي تستخدم Kubeflow على TheirStack.com.
بناءً على بياناتنا، فإن Kubeflow هو الأكثر شهرة في الولايات المتحدة (1,038 companies), المملكة المتحدة (182 companies), ألمانيا (79 companies), الهند (77 companies), فرنسا (71 companies), كندا (63 companies), البرازيل (44 companies), إسبانيا (39 companies), أستراليا (30 companies), سنغافورة (26 companies). ومع ذلك، فإنه يستخدم من قبل الشركات في جميع أنحاء العالم.
يمكنك العثور على الشركات التي تستخدم Kubeflow بالبحث عنها على TheirStack.com، نحن نتبع إعلانات الوظائف من ملايين الشركات ونستخدمها لاكتشاف التقنيات والأدوات الداخلية التي يستخدمونها.