Databricks ML Data Scientist Databricks-Machine-Learning-Associate Question # 12 Topic 2 Discussion

AutoML platforms, such as the one available in Databricks Machine Learning, streamline various stages of the machine learning pipeline including feature engineering, model selection, hyperparameter tuning, and model evaluation. However, exploratory data analysis (EDA) is typically performed outside the AutoML process. EDA involves understanding the dataset, visualizing distributions, identifying anomalies, and gaining insights into data before feeding it into a machine learning pipeline. This step is crucial for ensuring that the data is clean and suitable for model training but is generally done manually by the data scientist.

References

Databricks documentation on AutoML: https://docs.databricks.com/applications/machine-learning/automl.html