Company | Country | Industry | Employees | Revenue |
---|---|---|---|---|
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
|
MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
3,347
Unternehmen
Wir haben Daten zu 3,347 Unternehmen, die MLflow verwenden. Unsere MLflow 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 |
---|---|---|---|---|
Shopify | Kanada | Software Development | 19K | $4.8B |
Databricks | Vereinigte Staaten | Software Development | 8.8K | $600M |
Sanofi | Frankreich | Pharmaceutical Manufacturing | 92K | $46B |
Veeva Systems | Vereinigte Staaten | Software Development | 8.4K | $2.2B |
Peloton | Kanada | Oil And Gas | 5K | $2.8B |
Adevinta Group | Spanien | Online Audio And Video Media | 5K | $932M |
sennder | Deutschland | Truck Transportation | 970 | $350M |
ICF | Vereinigte Staaten | Business Consulting And Services | 11K | |
Prudential | Vereinigte Staaten | Financial Services | 41K | $65B |
FIS | Vereinigte Staaten | It Services And It Consulting | 46K | $14B |
Datamics GmbH | Deutschland | Software Development | 10 | |
Turo | Vereinigte Staaten | Technology, Information And Internet | 1.7K | $150M |
Möchten Sie die gesamte Liste herunterladen?
Melden Sie sich an und laden Sie die vollständige Liste der 3,347 Unternehmen herunter.
Loading countries...
Loading other techonlogies...
Nutzungsstatistiken für Technologie und Marktanteil
Sie können diese Daten an Ihre Bedürfnisse anpassen, indem Sie nach Geografie, Branche, Unternehmensgröße, Umsatz, Technologienutzung, Jobpositionen und mehr filtern. Sie können die Daten im Excel- oder CSV-Format herunterladen.
Sie können Alarme für diese Daten erhalten. Sie können beginnen, indem Sie die Technologie auswählen, die Sie interessiert, und dann erhalten Sie Alarme in Ihrem Posteingang, wenn es neue Unternehmen gibt, die diese Technologie verwenden.
Sie können seine Daten in eine Excel-Datei exportieren, die in Ihr CRM importiert werden kann. Sie können die Daten auch an eine API exportieren.
Es gibt 76 Alternativen zu MLflow
21,6k
19,5k
6k
3,6k
2,4k
2,3k
2k
1,8k
1,6k
1,3k
1,2k
1,1k
900
851
781
761
680
579
555
538
516
486
459
307
253
248
218
205
145
144
143
131
125
109
106
91
73
68
67
50
49
44
37
30
22
19
18
17
15
13
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.
MLflow is an open-source platform designed to manage the end-to-end machine learning lifecycle. It provides a framework to streamline the machine learning process, from experimentation to deployment, enabling Data Scientists to track experiments, package code, and deploy models efficiently. With its suite of tools and libraries, MLflow aims to simplify the complexities associated with machine learning development.
MLflow falls under the category of Machine Learning Tools, focusing on enhancing the development and deployment of machine learning models. It offers features such as experiment tracking, model packaging, and model serving, catering to the needs of Data Scientists and Machine Learning Engineers. By providing a centralized platform for managing the machine learning lifecycle, MLflow contributes to improved collaboration and productivity within data science teams.
Founded in June 2018 by the Databricks team, including the creators of Apache Spark, MLflow was established with the goal of addressing the challenges faced by organizations in operationalizing machine learning projects. The primary motivation behind the creation of MLflow was to offer a standardized approach to managing, deploying, and scaling machine learning models effectively. Since its inception, MLflow has gained significant traction in the machine learning community, attracting a growing user base and fostering a vibrant ecosystem around the platform.
In terms of current market share, MLflow has seen widespread adoption across various industries, thanks to its robust set of features and integrations with popular machine learning frameworks. As more organizations recognize the importance of streamlining their machine learning workflows, MLflow is poised for continued growth in the future. With advancements in technologies such as AI and data science, the demand for efficient machine learning tools like MLflow is expected to rise, further solidifying its position in the market.
Machine learning has become an increasingly essential tool for companies looking to gain insights from their data and make data-driven decisions. MLflow has emerged as a popular open-source platform for managing the end-to-end machine learning lifecycle. Companies use MLflow for various reasons, including streamlining workflows, improving collaboration, and enhancing model reproducibility.
MLflow provides a seamless environment for tracking experiments, packaging code, and deploying models, all within a unified platform. This streamlined approach simplifies the process of developing and deploying machine learning models, saving time and increasing productivity compared to using multiple tools for different tasks.
By enabling teams to log and share experiments, MLflow fosters collaboration among data scientists and engineers. This centralized platform ensures that all team members have access to the latest models and experiments, facilitating knowledge sharing and driving innovation across the organization.
MLflow's ability to capture dependencies and reproduce runs enables users to easily replicate and build upon past experiments. This ensures that results are consistent and reproducible, which is crucial for maintaining the integrity of machine learning projects compared to manual tracking methods.
In conclusion, the benefits of MLflow go beyond just effective model management; by streamlining workflows, enhancing collaboration, and ensuring model reproducibility, MLflow empowers companies to leverage machine learning more efficiently and effectively than other similar technologies in the market.
MLflow has become a widely adopted tool in the machine learning community, with several prominent companies leveraging its capabilities for managing their machine learning lifecycle. Let's dive into some real-world case studies of companies using MLflow:
1. Airbnb: Airbnb, the popular online marketplace for lodging and tourism experiences, utilizes MLflow to streamline their machine learning workflows. The company started using MLflow in 2018 to improve model management, experiment tracking, and deployment. With MLflow, Airbnb has been able to enhance collaboration among data scientists and engineers, leading to faster model iteration cycles and more efficient deployment processes.
2. Databricks: Databricks, a leading provider of unified data analytics platform, relies on MLflow to empower their data science teams with advanced model management capabilities. They integrated MLflow into their platform in 2019, enabling seamless tracking of experiments, model versioning, and deployment at scale. By leveraging MLflow, Databricks has been able to accelerate the development of machine learning models and ensure reproducibility across different projects.
3. Netflix: Netflix, the renowned streaming service provider, has incorporated MLflow into their machine learning infrastructure to drive innovation in personalized recommendations and content optimization. Since 2020, Netflix has been using MLflow to manage experiments, track model performance, and deploy production-ready models efficiently. By harnessing the power of MLflow, Netflix has been able to continuously enhance the user experience through data-driven insights and algorithmic improvements.
These case studies highlight how companies across various industries leverage MLflow to enhance their machine learning capabilities and drive impactful business outcomes. By adopting MLflow, organizations can effectively manage the end-to-end machine learning lifecycle, collaborate more effectively across teams, and accelerate the development and deployment of machine learning models.
Sie können eine aktuelle Liste von Unternehmen, die MLflow 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 3,347 Unternehmen, die MLflow verwenden.
MLflow wird von einer Vielzahl von Organisationen in verschiedenen Branchen, einschließlich "Software Development", "Software Development", "Pharmaceutical Manufacturing", "Software Development", "Oil And Gas", "Online Audio And Video Media", "Truck Transportation", "Business Consulting And Services", "Financial Services", "It Services And It Consulting", verwendet. Für eine umfassende Liste aller Branchen, die MLflow nutzen, besuchen Sie bitte TheirStack.com.
Einige der Unternehmen, die MLflow verwenden, umfassen Shopify, Databricks, Sanofi, Veeva Systems, Peloton, Adevinta Group, sennder, ICF, Prudential, FIS und viele mehr. Sie können eine vollständige Liste von 3,347 Unternehmen, die MLflow nutzen, auf TheirStack.com finden.
Basierend auf unseren Daten ist MLflow am beliebtesten in Vereinigte Staaten (1,263 companies), Vereinigtes Königreich (264 companies), Frankreich (136 companies), Deutschland (136 companies), Kanada (109 companies), Indien (101 companies), Spanien (76 companies), Brasilien (54 companies), Niederlande (39 companies), Australien (35 companies). Es wird jedoch von Unternehmen auf der ganzen Welt verwendet.
Sie können Unternehmen, die MLflow 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.
MLflow wird in 63 Ländern verwendet