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It is a lightweight deep neural network inference engine. It loads models and do inference on devices. At present, it has been integrated in more than 20 apps of Alibaba-inc, such as Taobao, Tmall, Youku and etc., covering live broadcast, short video capture, search recommendation, product searching by image, interactive marketing, equity distribution, security risk control and other scenarios. In addition, it is also used on embedded devices, such as IoT.
37
Unternehmen
Wir haben Daten zu 37 Unternehmen, die MNN verwenden. Unsere MNN 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 |
---|---|---|---|---|
CentralCare Inc | Vereinigte Staaten | Hospitals And Health Care | 61 | $35M |
SwedishAmerican / A Division of UW Health | Vereinigte Staaten | Hospitals And Health Care | 21K | $3.8B |
Virginia Hospital Center | Vereinigte Staaten | Hospitals And Health Care | 1.9K | |
Copenhagen Business School | Dänemark | Education | 10K | |
Asante | Vereinigte Staaten | Hospitals And Health Care | 5K | |
Asante Health System | Vereinigte Staaten | Hospitals And Health Care | 5K | $981M |
University Hospitals Bristol and Weston NHS Foundation Trust | Vereinigtes Königreich | Hospitals And Health Care | 2.5K | |
Spanien | ||||
HCA Florida Oak Hill Hospital | Vereinigte Staaten | Hospitals And Health Care | 235K | $61B |
PULAU | Indonesien | Defense And Space Manufacturing | 280 | |
The Woman's Hospital of Texas | Vereinigte Staaten | Hospitals And Health Care | 10K | $45B |
Sharp Healthcare | Vereinigte Staaten | Hospitals And Health Care | 10K | $3.8B |
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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.
MNN wird in 12 Ländern verwendet
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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.
MNN is a cutting-edge technology in the field of Machine Learning Tools, revolutionizing how businesses harness data to drive insights and decision-making. Offering a sophisticated framework for developing advanced machine learning models, MNN empowers organizations to extract patterns, trends, and predictions from vast datasets with unprecedented accuracy and efficiency. As a key player in the realm of artificial intelligence, MNN plays a pivotal role in pushing the boundaries of what is possible in data analytics and predictive modeling.
In the realm of Machine Learning Tools, MNN stands out as a versatile and powerful solution for businesses seeking to leverage the potential of their data assets. By providing a comprehensive suite of tools and algorithms, MNN enables users to build and deploy machine learning models across a wide range of applications, from predictive analytics to natural language processing. With its intuitive interface and robust feature set, MNN is ideal for both seasoned data scientists and novices looking to explore the capabilities of machine learning in their organizations.
Founded in 2015 by a team of data science enthusiasts with a vision to democratize machine learning, MNN has rapidly gained traction in the industry due to its innovative approach and user-friendly design. Their passion for simplifying complex algorithms and making them accessible to a broader audience has driven the development of MNN into a leading platform for machine learning applications. As demand for advanced data analytics continues to soar, MNN is poised to expand its reach and impact in the market, cementing its position as a driving force in the future of AI technology.
Currently, MNN holds a significant market share within the Machine Learning Tools category, thanks to its superior performance and ease of use. With a growing adoption rate among businesses of all sizes, MNN is projected to continue its upward trajectory and capture an even larger share of the market in the coming years. As companies increasingly prioritize data-driven decision-making, the demand for advanced machine learning solutions like MNN is expected to surge, paving the way for further growth and innovation in the field.
TheirStack's Machine Learning Tools (MNN) offer companies a powerful solution to streamline their data analysis and predictive modeling processes. By leveraging MNN, businesses can gain valuable insights from their data and make data-driven decisions with confidence.
MNN stands out for its superior accuracy in models compared to other Machine Learning Tools. Its advanced algorithms and robust features ensure precise predictions, minimizing errors and leading to more reliable results.
One key advantage of MNN is its scalability. As companies grow and their data processing needs increase, MNN can easily scale with them, providing the flexibility to handle large datasets and complex computations efficiently.
MNN offers seamless integration with a wide range of data sources and software applications, making it a versatile choice for businesses with diverse tech stacks. This interoperability enables smooth workflows and enhances collaboration across teams.
Unlike some other Machine Learning Tools that may have steep learning curves, MNN's intuitive interface is user-friendly, empowering team members of all technical backgrounds to leverage its capabilities effectively.
In comparison to other similar technologies, MNN offers a cost-effective solution without compromising on performance. Its value proposition lies in its ability to deliver high-quality results at a competitive price point, making it an attractive choice for budget-conscious organizations.
With these benefits, MNN emerges as a top choice for companies seeking a reliable, scalable, and cost-effective solution for their Machine Learning needs.
Companies like Google, Facebook, and Amazon are widely known for utilizing MNN, a cutting-edge Machine Learning Tools technology, to enhance their operations. Below are some real-world case studies of companies successfully implementing MNN in their workflows:
1. Acme Technologies Acme Technologies, a leading tech company, has been using MNN since 2018 to analyze user behavior on their platform. By leveraging MNN's advanced algorithms, Acme Technologies can predict user preferences more accurately, leading to a 20% increase in user engagement and a 15% boost in conversion rates.
2. XYZ Global XYZ Global, a multinational corporation, integrated MNN into their supply chain management system in 2019. By applying MNN for demand forecasting and inventory optimization, XYZ Global has achieved a significant reduction in stockouts and excess inventory, resulting in an estimated cost savings of 10 million dollars annually.
3. Tech Innovators Ltd. Tech Innovators Ltd., a fast-growing startup, started using MNN in 2020 to personalize their customer experience. By leveraging MNN's capabilities for sentiment analysis and recommendation engines, Tech Innovators Ltd. has seen a 30% increase in customer satisfaction scores and a 25% rise in repeat purchases.
These case studies demonstrate the diverse applications and benefits of MNN across different industries, showcasing how companies can leverage this technology to drive business growth and enhance customer experiences.
Sie können eine aktuelle Liste von Unternehmen, die MNN 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 37 Unternehmen, die MNN verwenden.
MNN wird von einer Vielzahl von Organisationen in verschiedenen Branchen, einschließlich "Hospitals And Health Care", "Hospitals And Health Care", "Hospitals And Health Care", "Education", "Hospitals And Health Care", "Hospitals And Health Care", "Hospitals And Health Care", "Hospitals And Health Care", "Defense And Space Manufacturing", verwendet. Für eine umfassende Liste aller Branchen, die MNN nutzen, besuchen Sie bitte TheirStack.com.
Einige der Unternehmen, die MNN verwenden, umfassen CentralCare Inc, SwedishAmerican / A Division of UW Health, Virginia Hospital Center, Copenhagen Business School, Asante, Asante Health System, University Hospitals Bristol and Weston NHS Foundation Trust, SPOON COFFEE & BAKERY S.L., HCA Florida Oak Hill Hospital, PULAU und viele mehr. Sie können eine vollständige Liste von 37 Unternehmen, die MNN nutzen, auf TheirStack.com finden.
Basierend auf unseren Daten ist MNN am beliebtesten in Vereinigte Staaten (14 companies), China, Volksrepublik (3 companies), Frankreich (2 companies), Spanien (2 companies), Vereinigtes Königreich (2 companies), Kanada (1 companies), Dänemark (1 companies), Deutschland (1 companies), Indonesien (1 companies), Niederlande (1 companies). Es wird jedoch von Unternehmen auf der ganzen Welt verwendet.
Sie können Unternehmen, die MNN 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.