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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
Unternehmen
Wir haben Daten zu 2,423 Unternehmen, die Kubeflow verwenden. Unsere Kubeflow Kundenliste steht zum Download bereit und ist mit wichtigen Unternehmensspezifika angereichert, darunter Branchenklassifikation, Organisationsgröße, geografische Lage, Finanzierungsrunden und Umsatzzahlen, unter anderem.
Unternehmen | Land | Branche | Mitarbeiter | Umsatz |
---|---|---|---|---|
Tripadvisor | Vereinigte Staaten | Software Development | 5.8K | $777M |
Zscaler | Vereinigte Staaten | Computer And Network Security | 8.1K | $1.1B |
Okta | Vereinigte Staaten | Software Development | 7.7K | $1.3B |
Rokt | Vereinigte Staaten | Software Development | 585 | |
Bain & Company | Vereinigte Staaten | Business Consulting And Services | 22K | $6B |
Canonical | Vereinigtes Königreich | Software Development | 1.5K | $126M |
Ayasdi | Vereinigte Staaten | Software Development | 50 | $12M |
Affirm | Vereinigte Staaten | Financial Services | 2.6K | $1.2B |
Wayfair | Vereinigte Staaten | Retail | 14K | $12B |
Seldon | Vereinigtes Königreich | Embedded Software Products | 110 | $11M |
UKG | Vereinigte Staaten | Software Development | 15K | $1.5B |
Grupo Boticário | Brasilien | Wellness And Fitness Services | 29K | $1.4B |
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Nutzungsstatistiken für Technologie und Marktanteil
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Kubeflow wird in 57 Ländern verwendet
Häufig gestellte Fragen
Unsere Daten stammen aus Stellenanzeigen, die von Millionen von Unternehmen gesammelt wurden. Wir überwachen diese Anzeigen auf Firmenwebseiten, Jobbörsen und anderen Rekrutierungsplattformen. Die Analyse von Stellenanzeigen bietet eine zuverlässige Methode, um die von Unternehmen verwendeten Technologien zu verstehen, einschließlich der Nutzung interner Tools.
Wir aktualisieren unsere Daten täglich, um sicherzustellen, dass Sie auf die aktuellsten verfügbaren Informationen zugreifen. Dieser häufige Aktualisierungsprozess garantiert, dass unsere Einsichten und Erkenntnisse die neuesten Entwicklungen und Trends der Branche widerspiegeln.
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.
Sie können eine aktuelle Liste von Unternehmen, die Kubeflow verwenden, auf TheirStack.com einsehen. Unsere Plattform bietet eine umfassende Datenbank von Unternehmen, die verschiedene Technologien und interne Tools nutzen.
Bis jetzt haben wir Daten von 2,423 Unternehmen, die Kubeflow verwenden.
Kubeflow wird von einer Vielzahl von Organisationen in verschiedenen Branchen, einschließlich "Software Development", "Computer And Network Security", "Software Development", "Software Development", "Business Consulting And Services", "Software Development", "Software Development", "Financial Services", "Retail", "Embedded Software Products", verwendet. Für eine umfassende Liste aller Branchen, die Kubeflow nutzen, besuchen Sie bitte TheirStack.com.
Einige der Unternehmen, die Kubeflow verwenden, umfassen Tripadvisor, Zscaler, Okta, Rokt, Bain & Company, Canonical, Ayasdi, Affirm, Wayfair, Seldon und viele mehr. Sie können eine vollständige Liste von 2,423 Unternehmen, die Kubeflow nutzen, auf TheirStack.com finden.
Basierend auf unseren Daten ist Kubeflow am beliebtesten in Vereinigte Staaten (1,038 companies), Vereinigtes Königreich (182 companies), Deutschland (79 companies), Indien (77 companies), Frankreich (71 companies), Kanada (63 companies), Brasilien (44 companies), Spanien (39 companies), Australien (30 companies), Singapur (26 companies). Es wird jedoch von Unternehmen auf der ganzen Welt verwendet.
Sie können Unternehmen, die Kubeflow verwenden, finden, indem Sie auf TheirStack.com danach suchen. Wir verfolgen Stellenanzeigen von Millionen von Unternehmen und nutzen sie, um herauszufinden, welche Technologien und internen Tools sie verwenden.