STADLE model orchestration platform optimizes enterprise data use for scalable model training, secure cross-silo training to enhance performance, and integrate new data for faster model deployment.
KEY FEATURES
Model 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, and organize AI models, ensuring access to the best-performing and latest versions
MODEL VALIDATION
Track and analyze AI model performance to ensure accuracy, reliability, and continuous improvement
MODEL AGGREGATION
Integrate new learning results from multiple ML environments to maintain a best performing AI model
MODEL TRAINING SCALABILITY
Kubernetes-enabled auto-scaling feature allows for the connection of an unlimited number of devices
MODEL DISTRIBUTION
Automatically deploy the best AI models to all devices, ensuring seamless and up-to-date performance.
To start using STADLE
1
Sign up in our user portal here. After that, purchase our trial/basic license or contact us to see if there is any free license available.
2
Go to STADLE Dashboard, use the sing-up info from the user portal to login. You can check if your license is active on your User Prifile page.
3
Create a project and initiate an aggregator to activate the STADLE functionality. Explore the User Guide to learn various functionalities.
Core Technologies in Place
STADLE platform utilizes the advanced ML technologies such as distributed and federated learning to further accelerate the model training process making it really secure and flexible.
Federated Learning
Federated Learning (FL), also called Distributed Learning, 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 privacy is preserved and the level of intelligence is not compromised. FL can be applied not only to mobile services but also to all services where customers’ privacy and scalability comes into the picture. TieSet has succeeded in developing the world’s first fully decentralized federated learning technology.
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.
