KubeML: An Efficient Serverless Platform for Scalable Deep Learning

Serverless computing is an emerging paradigm for structuring applications in such a way that they can benefit from on-demand computing resources and achieve horizontal scalability. As such, it is an ideal substrate for the resource-intensive and often ad-hoc task of training deep learning models. However, the design and stateless nature of...

Evaluating and Improving Large-Scale Machine Learning Frameworks

Given the increasing popularity of Machine Learning, and the ever increasing need to solve larger and more complex learning challenges, it is unsurprising that numerous distributed learning strategies have been brought forward in recent years, along with many large scale Machine Learning frameworks. It is however unclear how well these...