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MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
3,347
aziende
Abbiamo dati su 3,347 aziende che usano MLflow. La nostra lista di clienti MLflow è disponibile per il download ed è arricchita con specifiche vitali dell'azienda, incluse classificazione industriale, dimensioni organizzative, posizione geografica, round di finanziamenti e cifre di ricavi, tra gli altri.
Azienda | Paese | Settore | Dipendenti | Entrate |
---|---|---|---|---|
Shopify | Canada | Software Development | 19K | $4.8B |
Databricks | Stati Uniti | Software Development | 8.8K | $600M |
Sanofi | Francia | Pharmaceutical Manufacturing | 92K | $46B |
Veeva Systems | Stati Uniti | Software Development | 8.4K | $2.2B |
Peloton | Canada | Oil And Gas | 5K | $2.8B |
Adevinta Group | Spagna | Online Audio And Video Media | 5K | $932M |
![]() sennder | Germania | Truck Transportation | 970 | $350M |
ICF | Stati Uniti | Business Consulting And Services | 11K | |
Prudential | Stati Uniti | Financial Services | 41K | $65B |
FIS | Stati Uniti | It Services And It Consulting | 46K | $14B |
Datamics GmbH | Germania | Software Development | 10 | |
![]() Turo | Stati Uniti | Technology, Information And Internet | 1.7K | $150M |
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MLflow è utilizzata in 63 paesi
Domande frequenti
I nostri dati provengono da offerte di lavoro raccolte da milioni di aziende. Monitoriamo queste offerte sui siti web delle aziende, sui portali di lavoro e su altre piattaforme di reclutamento. Analizzare le offerte di lavoro offre un metodo affidabile per comprendere le tecnologie impiegate dalle aziende, inclusi i loro strumenti interni.
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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.
Puoi accedere a un elenco aggiornato di aziende che utilizzano MLflow visitando TheirStack.com. La nostra piattaforma fornisce un database completo di aziende che utilizzano varie tecnologie e strumenti interni.
Fino ad ora, abbiamo dati su 3,347 aziende che utilizzano MLflow.
MLflow è utilizzato da una vasta gamma di organizzazioni in vari settori, inclusi "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". Per un elenco completo di tutti i settori che utilizzano MLflow, si prega di visitare TheirStack.com.
Alcune delle aziende che utilizzano MLflow includono Shopify, Databricks, Sanofi, Veeva Systems, Peloton, Adevinta Group, sennder, ICF, Prudential, FIS e molte altre. Puoi trovare un elenco completo di 3,347 aziende che utilizzano MLflow su TheirStack.com.
Secondo i nostri dati, MLflow è più popolare in Stati Uniti (1,263 companies), Regno Unito (264 companies), Francia (136 companies), Germania (136 companies), Canada (109 companies), India (101 companies), Spagna (76 companies), Brasile (54 companies), Paesi Bassi (39 companies), Australia (35 companies). Tuttavia, è utilizzato da aziende in tutto il mondo.
Puoi trovare aziende che utilizzano MLflow cercandolo su TheirStack.com. Tracciamo le offerte di lavoro di milioni di aziende e le utilizziamo per scoprire quali tecnologie e strumenti interni stanno utilizzando.