Kubeflow is the de facto standard for running Machine Learning workflows on Kubernetes. Jupyter Notebook is a very popular tool that data scientists use every day to write their ML code, experiment, and visualize the results. However, when it comes to converting a Notebook to a Kubeflow Pipeline, data scientists struggle a lot. It is a very challenging, time-consuming task, and most of the time it needs the cooperation of several different subject-matter experts: Data Scientist, Machine Learning Engineer, Data Engineer.
This webinar will guide you through a seamless workflow that enables data scientists to deploy a Jupyter Notebook as a Kubeflow pipeline with the click of a button. Moreover, we will showcase how a data scientist can reproduce a step of the pipeline run, debug it, and then re-run the pipeline without having to write a single line of code. We will focus on two essential aspects:
-Low barrier to entry: convert a Jupyter Notebook to a multi-step Kubeflow pipeline in the Cloud using only the GUI.
-Reproducibility: automatic data versioning to enable reproducibility and better collaboration between data scientists.