AutoML on Databricks: Augmenting Data Science from Data Prep to Operationalization - The Databricks Blog

Thousands of data science jobs are going unfilled today as global demand for the talent greatly outstrips supply. Every day, businesses pay the price of the data scientist shortage in missed opportunities and slow innovation. For organizations to realize the full potential of machine learning, data teams have to build hundreds of predictive models a year. For most enterprises, only a fraction of that number is actually achieved due to understaffed data science teams. Databricks can help data science teams be more productive by automating various steps of the data science workflow – including feature engineering, hyperparameter tuning, model search, and deployment – for a fully controlled and transparent augmented ML experience.