Solutions
As a R&D partner we help customers develop machine learning solutions. With Scaleout on your team you stay on top of latest research in decentralised and privacy-preserving AI.
As a R&D partner we help customers develop machine learning solutions. With Scaleout on your team you stay on top of latest research in decentralised and privacy-preserving AI.
There is a rapidly growing need for solutions that account for data security and data sovereignty. Privacy and personal integrity is becoming a key focus in sustainable AI, and ML is moving to the edge. Scaleout brings these prespectives and technical expertise in all stages of your AI journey.
Business Need
Autonomous driving systems need to enable federated on-device training of models in order to manage large scale, in-vehicle machine learning
Data Challenges
Collecting data from all cars in use is expensive and in many cases impossible due to connection problems and the sheer quantity of data generated by modern cars.
Why Federated Learning
Federated Learning addresses the challenges by training on-board machine learning models in a federated setting so that each single car can learn from individual, group and fleet data.
Business Need
The Air Traffic Management (ATM) industry is moving towards a digital European sky. Trajectory Based Operations (TBO) allows for the proper coordination of ATM constraints on traffic, before or during flight and the airspace users can fly the best trajectory possible safely and efficiently. The goal is to have more accurate trajectory predictions.
Data Challenges
TBO consist of seamless accurate prediction & optimisation of trajectories and ATM constrains through all the planning phases. Important relevant data spread out over several stakeholders in TBO is non-sharable data: too sensitive business data or protected by GDPR.
Why Federated Learning
Federated Learning addresses these challenges by enabling privacy- preserving exploitation of private data (relevant for operations) for ML purposes, while stakeholders keeping the full ownership and control in their own data silo.
Business Need
There is a large potential in using AI in medicine. Use case include digital pathology, segmentation of organs and tumor, and detecting anomalies in time series data. Today doctors manually generate annotated training data. The software needs to learn from this user generated data to improve machine learning models. Without the data, they do not reach the required scale of data to train high performing models.
Data Challenges
The problem is that the data is sensitive, owned by the customers, and subject to health data regulations. Data cannot be be moved off site and shared, and cannot be used effectively to improve AI software. Single clinics struggle with obtaining diverse enough data in large enough quantities. Anonymization and pooling of data is both costly, time consuming, and risks reducing data utility.
Why Federated Learning
With federated learning the regulatory challenge can be addressed and no data is moved off site. Without sharing the data, learnings from multiple clinics can be integrated to improve the machine learning models.
SciLifeLab, the leading Swedish infrastructure for data-driven life science, is building a collaborative, open source solution to enable the life science community in Sweden accelerate use of AI. Key needs are model serving and sharing, and delivery of various AI tools from on-premise infrastructure.
Together with the team at the SciLifeLab data center we are building a solution based on Scaleout platform, deployed to Kubernetes.
Tooling include Jupyter, MLFlow, RStudio, RShiny, Tensorflow Serving, TorchServe, Dash and Flask.
