The journey or the AI/ML lifecycle consists of several steps ranging from accessing the data to training the models and then deploying it. This process is an involved one and is a subject of rapid engineering (especially in open source) and research (e.g. OpML). In this tutorial, we articulate the technical challenges faced during the AI/ML lifecycle management by a variety of persona ranging from the ML scientist to the ML DevOps engineer. We introduce a consistent platform across multiple clouds called
Kubeflow, to help solve the challenges faced in multi-cloud AI/ML lifecycle management.
