Scaleout

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This website offers resources and workshops focused on federated learning, a method that allows secure AI training across multiple devices without centralizing data. Users can access tutorials, personal consultations, and hands-on workshops to learn about the FEDn framework and its applications in industries like automotive, defense, and cybersecurity.

Scaleout can be found in AutoML Platforms categories.

The most similar products to Scaleout are:

FullCertified Promoted → Duality → Outerbounds → NAIRR →

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Scaleout Main Features

• Tutorial
  Data scientists and machine learning professionals can start learning about FEDn by signing up for a free account and following the Getting Started tutorial.
• Workshop
  Join our hands-on workshop to learn the basics of federated learning through practical examples.
• Video Series
  Our videos make learning about federated learning easy for everyone, whether you are a beginner or an expert.
• Cybersecurity
  Using federated learning with other security methods can greatly improve the safety of AI applications, ensuring data privacy and protection against threats.
• 1-on-1 Session
  Book a personal meeting with our CTO to learn more about federated learning and how it can help your organization.
• FEDn Framework
  FEDn helps in developing federated learning from local testing to full deployment without needing to change the code.
• Investment Round
  Scaleout received funding to improve its FEDn framework for secure AI deployment in industries like IoT, automotive, and defense while keeping data safe.
• Edge Intelligence
  Federated Learning brings smart technology to every device, allowing safe and scalable AI from the cloud to the edge while keeping your data secure.
• Federated Learning
  Federated learning trains models on data that is spread out, which means it can work securely and efficiently on many devices without needing to gather all the data in one place.
• Automotive Solutions
  AI solutions for vehicles process large amounts of data while keeping information private, which is important for the future of smart cars.
• Defense Applications
  Federated learning is useful in defense because it allows for advanced technology while keeping operations secure.
• Python vs. C++ Clients
  FEDn supports both Python and C++ clients, with Python using less memory and C++ being faster and more efficient during training.
• Federated Learning Explained
  This video gives a simple explanation of federated machine learning, focusing on how it keeps data private and its benefits for different industries.
• Collaborations & Partnerships
  We work with many organizations on different federated learning projects, showcasing our diverse use cases.
• Scaleout Joins NATO's DIANA Programme
  Scaleout's project for NATO allows secure updates of machine learning models in conflict areas without risking sensitive data.
• Distributed Continuous Machine Learning
  Federated learning changes how AI works by allowing models to learn from data that is spread out, which helps keep data private and reduces delays for real-time processing.

Scaleout Benefits

• Scalability
  It allows AI to grow easily across many devices without needing to change the code, making it simple to expand.
• Data Privacy
  Federated learning keeps your data safe by not sending it to a central server, which helps protect your personal information.
• Collaboration
  It enables different devices to learn from each other without sharing sensitive data, promoting teamwork while keeping information private.
• Real-Time Processing
  By processing data on the device, federated learning reduces delays, allowing for faster responses in applications.
• Secure AI Applications
  Federated learning helps create secure AI solutions in industries like automotive and defense, ensuring safety and reliability.

Who should use Scaleout?

• Learners
  They can watch videos that explain federated learning simply, making it easier to understand.
• Developers
  They can use the FEDn framework to build AI applications without needing to change their code.
• Data Scientists
  They will gain skills in federated learning, helping them work with data without compromising privacy.
• Defense Experts
  They will find ways to use secure AI for military applications, keeping sensitive information safe.
• Business Leaders
  They can discover how federated learning can improve their organization's AI projects while keeping data secure.
• Automotive Professionals
  They can learn how to use AI safely in cars, making them smarter and more efficient.
• Cybersecurity Specialists
  They can improve the security of AI systems, protecting data from threats and ensuring privacy.

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Technologies Used

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