Scaleout Systems provides a federated machine learning platform for secure and collaborative AI development.

Scaleout Systems

Leverage distributed data for machine learning without the cost and risk of pooling data. 

Scaleout brings federated learning to MLOps. Our software platform lets you develop privacy-preserving solutions for computer vision, natural language processing, anomaly detection and more.

Scaleout Studio is a cloud native platform for federated machine learning operations with two main goals: To 1) reduce the technical complexity of secure federated learning for the data scientist, and 2) to scale federated machine learning in production. Your team collaborates in Studio-projects to define the federated model and are then able to add and manage clients and devices in the federation. Studio is highly customisable, just add the components you need as Helm charts. We collect the leading open source tools for model development, collaboration and model serving, and provide them out of the box with project-level multi-tenancy and object level permissions. Studio runs on any standard Kubernetes cluster, and can be deployed in any public or private cloud and on on-premise hardware. 

Read more at www.scaleoutsystems.com 
   
   

Scaleout

We invite researchers focusing on AI and medical imaging, as well as active members of the #MONAI community, to explore federated learning.

You can now set up your projects using a ready-to-use example based on the MedNIST dataset, leveraging the MONAI model in federated settings with the open FEDn federated learning framework.

Detailed instructions are available in both the MONAI tutorial and the FEDn example repository. With free signup, you can deploy this example in minutes via our public SaaS platform.

MONAI-tutorial: https://lnkd.in/dkDQk75h

Learn more about the FEDn framework: https://lnkd.in/driA7vSW 


#MedicalImaging #ArtificialIntelligence #HealthcareAI #FederatedLearning

Scaleout will be in Madrid Aug 26-28. 

Salman Toor and I will be in Madrid for the 30th International European Conference on Parallel and Distributed Computing (EURO-PAR). 

We would love to meet up and discuss how #federatedlearning can help scale machine learning projects when data cannot be pooled, and how this relates to #cybersecurity for AI.  DM us to set up a meeting, and do let us know if there is someone we should meet!

The Scaleout team is delighted to welcome Sowmya Sri Seenivasan as our new ML Engineer. With extensive experience in image detection and tracking, Sowmya is a fantastic addition to our team, and we’re excited to collaborate with her.

A warm welcome!

Explore the possibilities of running Flower Labs ClientApps on the FEDn platform in this YouTube tutorial!

This hands-on guide takes you through the entire process, from setting up the environment with FEDn to running your first federated learning client using Flower’s framework. Ideal for developers interested in federated learning and wanting to explore the capabilities of Scaleout and Flower Labs combined.

https://lnkd.in/dR4SgGYu

The example on our GitHub: https://lnkd.in/dfqnpKzM

More details about the Scaleout and Flower collaboration:
https://lnkd.in/dH-QddDf

#FederatedLearning 
#FEDn 
#Flowerlabs

I am thrilled to share that I am participating in a course at Tartu University to delve into the fascinating world of Federated Learning using the FEDn framework. 
A special shoutout to Prof Salman Toor, a member of the Scaleout company, for her insightful presentation on setting up a distributed computation architecture using FEDn. His practical examples and guidance have been invaluable.
If you are interested in federated learning or looking to scale your machine learning projects, I highly recommend exploring FEDn and considering similar opportunities.🚀

#FederatedLearning #FEDn #MachineLearning #Scaleout #DistributedComputing #Innovation #schedulingsytem

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