Databricks AutoML is designed to automate the iterative and time-consuming tasks of the machine learning model development process. This includes data preprocessing, model training, hyperparameter tuning, and evaluation. The output of an AutoML experiment is a set of trained models ranked on a leaderboard, along with generated notebooks. While AutoML facilitates the next step by allowing one-click registration of the best model into the MLflow Model Registry, the actual deployment of that model—for example, creating a real-time serving endpoint or setting up a batch inference job—is a distinct, subsequent step in the MLOps lifecycle that must be performed by the user outside of the AutoML experiment itself.

A. Model tuning: This is incorrect. A core feature of Databricks AutoML is to automatically perform hyperparameter tuning across a range of algorithms to find the best-performing model.

B. Model evaluation: This is incorrect. AutoML automatically evaluates each model trial using relevant metrics (e.g., F1 score for classification, RMSE for regression) and presents the results in a leaderboard for comparison.

D. Exploratory data analysis: This is incorrect. As part of its output, Databricks AutoML generates a data exploration notebook which includes a statistical summary and visualizations of the training dataset, thus performing a form of automated EDA.

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1. Databricks Official Documentation

"How Databricks AutoML works": This document outlines the automated steps performed by AutoML. The list includes: "Prepares the dataset for model training

" "Iterates to train and tune multiple models

" "Evaluates models

" and "Provides a Python notebook with the source code... including a data exploration notebook." Model deployment is not listed as an automated step.

Source: Databricks Machine Learning Guide > AutoML > How Databricks AutoML works.

2. Databricks Official Documentation

"Model serving with Databricks": This documentation describes model deployment as a separate process that occurs after a model has been trained and registered in the Model Registry. It details the steps to create and manage serving endpoints

which is distinct from the AutoML experiment workflow.

Source: Databricks Machine Learning Guide > MLflow > Model serving with Databricks.

3. The Big Book of MLOps (Databricks Ebook)

Chapter 2

"A Modern MLOps Architecture": This official resource presents a diagram of the MLOps lifecycle. The "Build & Train" stage

which includes AutoML

is shown as separate and preceding the "Model Deployment" and "Model Monitoring" stages. This clearly delineates deployment as an activity outside of the automated training experiment.

Source: Databricks Ebooks

"The Big Book of MLOps"

Page 15

Figure 2-1.