STADLE intelligence-orchestration platform enables data to stay local, while users' AI models are shared
KEY COMPONENTS
Federated, Distributed
& Continuous Learning
User-Friendly GUI & APIs
AI Model Repository
Enable New Business, Improve Efficiency, and Save Costs
Enabling New Business
Privacy Example
Privacy-preserving AI is the only way to create AI diagnosis business in Medical Record Learning
Personal home robots need privacy-preserving AI to learn and improve capabilities
Improve Efficiency
Self driving Car Example
Before: Upload 1GB per second, 10 TB per day
After: Upload only AI models 500MB per hour, 1.5 GB per day
About 6000 times efficient than current cloud-based solution
Cost Saving
Cloud Data Center Example
Investment: $200K STADLE License vs. $1M for 1,000 sq. ft. datacenter
Return: Annually save $800K on datacenter + network only for 1,000 sq. ft.
KEY FEATURES
An Intelligence-Orchestration Platform with Continuous & Collaborative Learning
You do not need to purchase costly servers or subscribe cloud platforms, we provide a comprehensive platform for you to use our cloud STADLE platform just by using our variety of APIs that instantly work with your local AI solutions and applications.
MODEL MANAGEMENT
Upload/download various AI models, including the best-performing and most recent models.
MODEL VALIDATION
Check outcomes and performance by tracking model performance
ONLINE MACHINE LEARNING
Combine our APIs to build an automated local ML app that continuously learns from the dynamics of data.
AUTO SCALING
Kubernetes-enabled auto-scaling feature allows for the connection of an unlimited number of devices.
MODEL DISTRIBUTION
Always distribute the best AI Models to all devices anytime
Technologies in Place
Federated Learning
Federated Learning (FL) has gained worldwide recognition after Google Research released a mobile application where all the training happens at mobile devices of users. The private data of users will not leave from distributed devices, and the local AI models are aggregated to provide collective intelligence. The cost to maintain big data is significantly reduced by FL, while the level of intelligence is not compromised. FL can be applied not only to mobile services but also to all services where customers’ privacy comes into the picture. TieSet has succeeded in developing the world’s first fully decentralized federated learning technology.
Privacy-Preserving AI
With the rapid growth of Artificial Intelligence technologies, concerns over consumer privacy have been increased to a large extent, especially in areas such as healthcare, home appliances, private pictures, and videos that user’s privacy is essential. To address privacy concerns, privacy communities must bring their knowledge to the machine learning field. Many privacy-enhancing techniques concentrated on allowing multiple users to collaboratively train ML models without exchanging local data. TieSet will lead the area of distributed AI so that a wide variety of people could benefit from the true power of AI.
Continuous Learning
Artificial Intelligence models have been designed and created in a static way in big data systems. However, intelligence is not a product of single-shot learning but needs to continuously grow with dynamic environment.
STADLE assists users to create dynamic distributed learning environments where the constant change and trend of data and behaviors can be absorbed with collaborative training processes.
Transfer Learning
When data is limited, Transfer Learning (TL) aims at improving performance in the accuracy or training time of an AI model in a target domain by using knowledge contained in a different but related source domain.
With TL we can deploy your AI solution faster and more efficiently by reusing previously generated models. Additionally, a system can learn a set of completely new tasks from the combination of previously acquired models by using a proprietary model synthesis engine.
