Managing Chaos: Reproducible Machine Learning on Databricks

Successfully building and deploying a machine learning model is difficult. Enabling other data scientists to reproduce your pipeline, compare the results of different versions, and rollback models is much harder. And when the underlying data that you used to train your model changes, this challenge becomes exponentially harder. This talk will introduce the open-source projects MLflow & Delta Lake as a solution for tracking and reproducing your ML experiments.