Scaling the Wall Between Data Scientist and Data Engineer - KDnuggets

One of the most exciting things in machine learning (ML) today, for me at least, is not at the bleeding-edge of deep learning or reinforcement learning. Rather it has more to do with how models are managed and how data scientists and data engineers effectively collaborate as teams. Navigating those waters will lead organisations towards a more effective and sustainable application of ML. Sadly, there is a divide between "scientist" and "engineer." "Building production machine learning applications is challenging because there is no standard way to record experiments, ensure reproducible runs, and manage and deploy models," says Databricks.