Solutions

Solutions for machine learning across use-cases and industries.

We can help you stay in control of your local data assets and efficiently use on premise and private cloud infrastructure for machine learning / MLOps.

Professional Services

Read about our services in ML, MLOps and federated / decentralized AI.

Download

Strategic advantage

There is a rapidly growing focus on 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 can help you build future-proof solutions in all stages of your AI journey.

  • Ensure independence of specific cloud providers.
  • Succeed with open source.
  • Put privacy-preserving ML on your agenda.
  • Partner with the developers of a world-leading platform for decentralized AI.

SciLifeLab Serve

SciLifeLab, the leading Swedish infrastructure for data-driven life science wanted a collaborative, open source solution for serving models and AI apps from on-premise infrastructure.

Together with the team at the SciLifeLab data center we built a multi-tenant solution based on our modular and lightweight platform for MLOps, deployed to Kubernetes.

Tooling include Jupyter, MLFlow, RStudio, RShiny, Tensorflow Serving, TorchServe, Dash and Flask.

Read more

Air traffic management

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.

Learn about AICHAIN

Edge learning

Business Need
Autonomous driving systems need to enable 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.

Read about FEDn in the EdgeLab