This section covers information about loading data specifically for ML and DL applications. For general information about loading data, see Ingest data into the Databricks Lakehouse.
Machine learning applications may need to use shared storage for data loading and model checkpointing. This is particularly important for distributed deep learning.
You can load tabular machine learning data from tables or files (for example, see CSV file). You can convert Apache Spark DataFrames into pandas DataFrames using the PySpark method
toPandas(), and then optionally convert to NumPy format using the pandas method