Amazon SageMaker Autopilot
Automatically create machine learning models with full visibility
Automatically build, train, and tune the best ML models based on your data, while maintaining full control and visibility
Select the best model from a leaderboard for model performance and accuracy requirements
Deploy the model to production with just one click or iterate with the recommended models in Amazon SageMaker Studio
Amazon SageMaker Autopilot eliminates the heavy lifting of building ML models. You simply provide a tabular dataset and select the target column to predict, and SageMaker Autopilot will automatically explore different solutions to find the best model. You then can directly deploy the model to production with just one click or iterate on the recommended solutions to further improve the model quality.
How it works
Key features
Automatic data pre-processing and feature engineering
You can use Amazon SageMaker Autopilot even when you have missing data. SageMaker Autopilot automatically fills in the missing data, provides statistical insights about columns in your dataset, and automatically extracts information from non-numeric columns, such as date and time information from timestamps.
Automatic ML model selection
Amazon SageMaker Autopilot automatically infers the type of predictions that best suit your data, such as binary classification, multi-class classification, or regression. SageMaker Autopilot then explores high-performing algorithms such as gradient boosting decision tree, feedforward deep neural networks, and logistic regression, and trains and optimizes hundreds of models based on these algorithms to find the model that best fits your data.
Model leaderboard
Amazon SageMaker Autopilot allows you to review all the ML models that are automatically generated for your data. You can view the list of models, ranked by metrics such as accuracy, precision, recall, and area under the curve (AUC), review model details such as the impact of features on predictions, and deploy the model that is best suited to your use case.
Feature importance
Amazon SageMaker Autopilot provides an explainability report, generated by Amazon SageMaker Clarify, that makes it easier for you to understand and explain how models created with SageMaker Autopilot make predictions. You can also identify how each attribute in your training data contributes to the predicted result as a percentage. The higher the percentage, the more strongly that feature impacts your model’s predictions.
Easy integration with your applications
You can use the Amazon SageMaker Autopilot application programming interface (API) to easily create models and make inferences right from your applications, such as your data analytics and data warehousing tools.
Customizable autoML journey
With Amazon SageMaker Autopilot, you can customize steps in your autoML journey to help create high quality ML models. You can apply your own data preprocessing and feature engineering transformations with 300+ pre-configured data transformations within SageMaker Data Wrangler and bring the recipe to SageMaker Autopilot. You can also define a custom data split for training and validation data or upload a custom dataset for validation. In addition, you can select features for training, change data type, and select a training mode (ensemble or hyperparameter optimization) for your SageMaker Autopilot experiment.
Easy integration with your applications
You can use the Amazon SageMaker Autopilot application programming interface (API) to easily create models and make inferences right from your applications, such as your data analytics and data warehousing tools.
Automatic notebook creation
You can automatically generate a Amazon SageMaker Studio Notebook for any model Amazon SageMaker Autopilot creates and dive into the details of how it was created, refine it as desired, and recreate it from the notebook at any point in the future.
Use Cases
Price predictions
Price prediction models are used heavily in financial services, real estate, and energy and utilities to predict the price of stocks, real estate, and natural resources. Amazon SageMaker Autopilot can predict future prices to help you make sound investment decisions based on your historical data such as demand, seasonal trends, and price of other commodities.
Churn prediction
Customer churn is the loss of customers or clients, and every company looks for ways to eliminate it. Models automatically generated by Amazon SageMaker Autopilot help you understand churn patterns. Churn prediction models work by first learning patterns in your existing data and identifying patterns in new datasets so you can get a prediction about customers mostly likely to churn.
Risk assessment
Risk assessment requires identifying and analyzing potential events that may negatively impact individuals, assets, and your company. Models automatically generated by Amazon SageMaker Autopilot predict risks as new events unfold. Risk assessment models are trained using your existing datasets so you can get optimized predictions for your business.
Customers
"At RetentionX, we provide one-click business insights to e-commerce companies. To serve our customers, it is important that they can get started quickly and make timely business decisions, however building accurate machine learning models can be costly and take months of trial-and-error. In addition, model accuracy is also highly dependent on the breadth and depth of training data and unique feature set available for each of our customers. With the help of Amazon SageMaker Autopilot, our customers can automatically generate the best ML models based on unique datasets. Thanks to SageMaker Autopilot, we can provide personalized insights to tens of millions of shoppers tapping into the power of AutoML."
Alexander Jost, CEO, RetentionX
"At EPCVIP, we leverage machine learning to better understand user group attributes, accelerate processing times, and to increase conversion rates for our product offerings. Fintech is a highly complex industry that is always changing. New partners, affiliates, traffic sources, and product offerings are added on a weekly basis. As we build machine learning models, we are constantly experimenting to adapt our models to produce superior results. Thanks to Amazon SageMaker Autopilot, we can now rapidly prototype and automatically build, train, and tune ML models with full visibility into the data. With SageMaker Autopilot, we can catalog and review features, hyperparameters, algorithms, and datasets, which has enabled us to fully understand our model iterations. The results from improved match rates and other deployed models through SageMaker Autopilot have contributed to our company’s value per lead by as much as 30%."
Pascal Simpkins, Head of Data Science, EPCVIP
"Sisense’s new ML service powered by Amazon SageMaker Autopilot was exactly what we needed to keep ahead of the curve in customer service during this COVID-19 pandemic. Skullcandy was able to gain deep insights into our customers’ needs, improve our issue resolution, and increase customer satisfaction scores."
Mark Hopkins, Chief Information Officer, Skullcandy Inc.
“Previously, we would simply pick two restaurants that looked similar, but now we have a true understanding of the relationships between our menu items, customers, and locations. Amazon SageMaker Autopilot, which powers Domo’s new ML capability, has been a force multiplier for our marketing and purchasing teams to try new ideas and improve our customers’ experience.”
Sean Thompson, IT Director, Freddy’s
"The primary goal in demographic mapping is optimizing across both accuracy and scale. While this is generally difficult, we were able to use Amazon SageMaker Autopilot with our comprehensive training data and sophisticated features to produce better models that improved our prediction accuracy by 137%."
Anindya Datta, CEO, Mobilewalla