With the rapid growth of AI, we all want to understand how to best leverage sources of data being generated by users to train our models. As developers, we must therefore either collect the data from the edge or learn to utilise that data locally to train models. The question is, how can we train machine learning (ML) models on data that exists at the edge?
To answer this, we will discuss different ML approaches such as transfer and federated learning. Join us to learn about how this allows models to be trained with edge data whilst preserving users’ privacy. Finally, we will turn our attention to cute dogs and show you a practical example of federated learning on mobile devices that sync to the cloud.
To answer this, we will discuss different ML approaches such as transfer and federated learning. Join us to learn about how this allows models to be trained with edge data whilst preserving users’ privacy. Finally, we will turn our attention to cute dogs and show you a practical example of federated learning on mobile devices that sync to the cloud.
Sai Prashant Vajja
Couchbase
Sai Vajja is an Solutions Engineer at Couchbase, the cloud data platform for modern applications, working with a range of enterprise customers helping them build modern, scalable & Multi-Cloud applications using NoSQL and DBaaS. Sai is a Postgraduate in computer sciences with over 20 years of experience in IT industry. Outside of work, Sai is passionate about motorcycling, photography and playing chess online.