AI models run on large amounts of good quality data, and when it comes to sensitive tasks like medical diagnosis or financial risk assessments, you need access to private data during both training and inference.

When performing machine learning tasks in the cloud, enterprises are understandably concerned about data privacy as well as their model’s intellectual property. Additionally, stringent industry regulations often prohibit the sharing of such data.

This makes it difficult, if not impossible, to utilise large amounts of valuable private data, limiting the true potential of AI across crucial domains.

In this webinar we’ll go through how confidential AI tackles this problem head on, providing a hardware-rooted execution environment that spans both the CPU and GPU.

With Ubuntu confidential AI, you can perform various tasks including ML training, inference, confidential multi-party data analytics and federated learning with confidence.