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A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
2,632
aziende
Abbiamo dati su 2,632 aziende che usano Amazon SageMaker. La nostra lista di clienti Amazon SageMaker 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 |
---|---|---|---|---|
Amazon Web Services, Inc. | Stati Uniti | It Services And It Consulting | 1.5M | $3M |
Amazon.com Services LLC | Stati Uniti | Retail | 10K | $50M |
Dice | Stati Uniti | Software Development | 736 | $12M |
Fidelity Investments | Stati Uniti | Financial Services | 77K | $25B |
![]() Policy Expert | Regno Unito | Insurance | 344 | $296K |
Brillio | Stati Uniti | It Services And It Consulting | 5.2K | $1B |
PwC | Regno Unito | Professional Services | 328K | $50B |
![]() Salesforce | Stati Uniti | Software Development | 80K | $32B |
EPAM Systems | Stati Uniti | It Services And It Consulting | 61K | $4.8B |
Chewy | Stati Uniti | Retail | 12K | $8.9B |
Qualitest | Regno Unito | It Services And It Consulting | 5.7K | |
Brainly | Polonia | Software Development | 820 | $14M |
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Amazon SageMaker is a comprehensive machine learning service provided by Amazon Web Services (AWS) that enables developers and data scientists to build, train, and deploy machine learning models quickly and at scale. With SageMaker, users have access to all the components needed for the entire machine learning workflow in a unified platform, including data processing, model training, and model hosting, making it easier to develop and deploy machine learning models.
Machine Learning as a Service (MLaaS) is a category that Amazon SageMaker falls under, offering a cloud-based platform for machine learning development and deployment without the need for users to manage the underlying infrastructure. This category provides tools and services that streamline the process of creating machine learning models, allowing businesses to leverage the power of artificial intelligence without the complexity typically associated with building and training models from scratch.
Amazon SageMaker was founded by Amazon Web Services and was officially launched in 2017. The motivation behind the creation of SageMaker was to simplify the machine learning workflow and make it more accessible to developers and organizations of all sizes. By providing a fully managed platform with built-in algorithms and tools, Amazon aimed to accelerate the adoption of machine learning in various industries and simplify the deployment of models into production environments.
As of the latest data available, Amazon SageMaker holds a significant market share within the Machine Learning as a Service category, with a growing number of businesses trusting the platform for their machine learning needs. The forecast indicates that Amazon SageMaker is likely to continue expanding its market share in the future, driven by the increasing demand for machine learning solutions and the continuous innovation and enhancements introduced by Amazon Web Services to the SageMaker platform.
Amazon SageMaker is a popular choice among companies looking to streamline their machine learning operations. With its comprehensive suite of tools and services, Amazon SageMaker simplifies the entire machine learning workflow, from data labeling and model training to deployment and monitoring.
Benefits of Amazon SageMaker:
1. Scalability:
Amazon SageMaker offers unmatched scalability, allowing companies to seamlessly scale their machine learning models to handle large datasets and increasing workloads. Unlike traditional machine learning platforms, SageMaker can automatically adjust resources based on demand, ensuring optimal performance at all times.
2. Cost-efficiency:
One of the key advantages of Amazon SageMaker is its cost-efficiency. By leveraging pay-as-you-go pricing models, companies can significantly reduce their infrastructure costs compared to setting up and maintaining costly on-premises machine learning environments or using other cloud-based alternatives.
3. Integration with AWS Services:
Amazon SageMaker seamlessly integrates with a wide range of AWS services, such as S3, Redshift, and Lambda, simplifying data access, storage, and deployment processes. This tight integration enables companies to build end-to-end machine learning pipelines without the need for complex integrations or third-party tools.
4. Built-in Algorithms and Frameworks:
Amazon SageMaker provides a rich library of built-in algorithms and popular machine learning frameworks like TensorFlow and PyTorch, empowering data scientists to quickly prototype and deploy models without having to manually install and configure libraries, saving time and reducing errors.
5. Automated Model Tuning:
Amazon SageMaker's automated model tuning capabilities eliminate the need for manual hyperparameter tuning, speeding up the model optimization process and helping companies achieve higher model accuracy and performance in less time compared to manual tuning methods.
Amazon SageMaker is a popular choice for companies looking to leverage machine learning as a service for their business needs. Several well-known companies have successfully integrated Amazon SageMaker into their operations, showcasing the platform's versatility and effectiveness. Below are a few case studies highlighting how some companies are utilizing Amazon SageMaker:
Bumble
Bumble, the popular dating and social networking platform, uses Amazon SageMaker to enhance its matching algorithms. By leveraging SageMaker's machine learning capabilities, Bumble has been able to optimize user recommendations, leading to better matches and increased user engagement. The company started using Amazon SageMaker in 2019 and has since seen significant improvements in user satisfaction.
Airbnb
Airbnb utilizes Amazon SageMaker for its fraud detection systems. By analyzing patterns and anomalies in user behavior data, Airbnb can proactively identify and prevent fraudulent activities on its platform. The integration of SageMaker has strengthened Airbnb's security measures and improved the overall trust and safety of its community. Airbnb began using Amazon SageMaker in 2018 and continues to refine its fraud detection processes with the platform's advanced machine learning capabilities.
Unilever
Unilever, a multinational consumer goods company, incorporates Amazon SageMaker into its product forecasting and demand planning processes. By analyzing historical sales data and market trends, Unilever can generate more accurate demand forecasts, optimize inventory management, and streamline its supply chain operations. Since adopting Amazon SageMaker in 2017, Unilever has experienced improved forecasting accuracy and greater operational efficiency across its global operations.
These case studies offer a snapshot of how companies across various industries are harnessing the power of Amazon SageMaker to drive innovation, efficiency, and growth in their businesses. By leveraging the capabilities of machine learning as a service, companies like Bumble, Airbnb, and Unilever are empowering themselves to make data-driven decisions and stay ahead in today's competitive market landscape.
Puoi accedere a un elenco aggiornato di aziende che utilizzano Amazon SageMaker 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 2,632 aziende che utilizzano Amazon SageMaker.
Amazon SageMaker è utilizzato da una vasta gamma di organizzazioni in vari settori, inclusi "It Services And It Consulting", "Retail", "Software Development", "Financial Services", "Insurance", "It Services And It Consulting", "Professional Services", "Software Development", "It Services And It Consulting", "Retail". Per un elenco completo di tutti i settori che utilizzano Amazon SageMaker, si prega di visitare TheirStack.com.
Alcune delle aziende che utilizzano Amazon SageMaker includono Amazon Web Services, Inc., Amazon.com Services LLC, Dice, Fidelity Investments, Policy Expert, Brillio, PwC, Salesforce, EPAM Systems, Chewy e molte altre. Puoi trovare un elenco completo di 2,632 aziende che utilizzano Amazon SageMaker su TheirStack.com.
Secondo i nostri dati, Amazon SageMaker è più popolare in Stati Uniti (1,191 companies), Regno Unito (173 companies), Canada (69 companies), India (57 companies), Francia (53 companies), Australia (51 companies), Germania (46 companies), Spagna (45 companies), Brasile (44 companies), Svizzera (23 companies). Tuttavia, è utilizzato da aziende in tutto il mondo.
Puoi trovare aziende che utilizzano Amazon SageMaker 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.
Amazon SageMaker è utilizzata in 50 paesi