The future of AI will be on cloud because of various reasons like scalability, computing power, features like Dynamic Resource Allocation and being able to run AI workloads across multiple clouds. However, the fields of cloud native and machine learning are still somewhat separated. We aim to bridge this gap with this tutorial.

We plan on giving a brief introduction to machine learning with scikit-learn and cloud native with Kubernetes, then dive into a hands-on tutorial. We will show different ways to train and run ML models on and off the cloud (CPUs vs GPUs, public cloud vs on-prem, different flavors of Kubernetes) and evaluate performance in terms of training speed and cost.

Additionally, training ML models is a complicated task and requires a lot of research. Optimizing your cloud setup on top of this makes it more difficult. There is no single solution, and we will explore a few of these options during this tutorial.