Train stronger machine learning models
Machine learning and analytics across organizational boundaries
Access federated data sets
Leverage existing model and data pipelines
Data custodians stay in control of data, always
Integrates with your current infrastructure and ML stack
Integral to your ML stack
Develop with the libraries you choose
Integrates seamlessly with a thriving ML ecosystem
You always stay in full control of your models
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Beyond MLOps - How Secure Data Collaboration Unlocks the Next Frontier of AI Innovation
DevOps and MLOps are common methodologies in every company that wants to become software and data science driven by weaving AI into the core fabric of their business. Read what is required to securely collaborate with partners on data and AI at scale.
The Future of Data Products – 6 Industry Trends For Frontrunners
If you’ve been successful to date with building industry-leading data products, you don’t want to rest on your laurels. Instead, you should be trying to extend your lead. However, this can be daunting task in an industry that is in constant flux as the data, machine learning and AI landscape.
E-book - Federated Data Ecosystems in Pharma & Healthcare
Breakthroughs in healthcare are faster and more reliable with federated data ecosystems. By processing patient data without risking its integrity, data collaboration is safer and more effective than data sharing. This e-book highlights real-world examples and explains how to implement a federated data ecosystem in pharma and healthcare.