Videos: Use Autopilot to automate and explore the machine learning process

Here is a video series that provides a tour of Amazon SageMaker Autopilot capabilities using Studio Classic. They show how to start an AutoML job, analyze and preprocess data, how to do feature engineering and hyperparameter optimization on candidate models, and how to visualize and compare the resulting model metrics.

Start an AutoML job with Amazon SageMaker Autopilot

This video shows you to how to start an AutoML job with Autopilot. (Length: 8:41)

Review data exploration and feature engineering automated in Autopilot.

This video shows you how to review the data exploration and candidate definition notebooks generated by Amazon SageMaker Autopilot. (Length: 10:04)

Tune models to optimize performance

This video shows you how to optimize model performance during training using hyperparameter tuning. (Length: 4:59)

Choose and deploy the best model

This video shows you how to use job metrics to choose the best model and then how to deploy it. (Length: 5:20)

Amazon SageMaker Autopilot tutorial

This video walks you through an end to end demo where we first build a binary classification model automatically with Amazon SageMaker Autopilot. We see how candidate models have been built and optimized using auto-generated notebooks. We also look at the top candidates with Amazon SageMaker Experiments. Finally, we deploy the top candidate (based on XGBoost), and configure data capture with SageMaker Model Monitor.