Mastering Databricks Machine Learning: A Comprehensive Guide

<a href="https://docs.databricks.com/machine-learning/feature-store/index.html" rel="noopener ugc nofollow" target="_blank">Databricks feature store</a> serves as a centralized repository that empowers data scientists to discover and collaborate on features, preventing data fragmentation. It ensures consistency by using the same code for both feature value computation during model training and inference, avoiding discrepancies between online and offline data. In Databricks, feature tables are constructed as PySpark DataFrames. The “fs.create_table” method requires either a schema or a DataFrame to be provided. On the other hand, the “fs.write_table” method supports both overwrite and merge modes. Notably, the Databricks Feature Store excels in handling time series data. By simply specifying “timestamp_keys=’ts’” when creating a feature table, users can effortlessly manage and share point-in-time feature data. When using “fs.drop_table”, not only the feature table but also the underlying Delta tables are removed. To retain the Delta tables while dropping the feature table, you should navigate to the Feature Store UI and click on the “Delete” button. <a href="https://medium.com/@chenycy/the-complete-introduction-of-databricks-machine-learning-66f81487e8a9">Visit Now</a>