Amazon SageMaker JumpStart
Pre-built machine learning (ML) solutions that you can deploy with just a few clicks
Amazon SageMaker JumpStart helps you quickly and easily get started with machine learning. To make it easier to get started, SageMaker JumpStart provides a set of solutions for the most common use cases that can be deployed readily with just a few clicks. The solutions are fully customizable and showcase the use of AWS CloudFormation templates and reference architectures so you can accelerate your ML journey. Amazon SageMaker JumpStart also supports one-click deployment and fine-tuning of more than 150 popular open source models such as natural language processing, object detection, and image classification models.
Use Cases
Discover the possibilities of Amazon SageMaker JumpStart
Predictive maintenance
Take preventive actions such as part replacement and servicing at the most optimal time, extending the remaining useful life of your machinery at the lowest cost and improving operational efficiency.
Computer vision
Bring automation or intelligent augmentation to a wide variety of applications to improve quality and speed.
Autonomous driving
Detect objects such as pedestrians and other vehicles on the road to accelerate the pace of innovation and bring self-driving vehicles to life.
Fraud detection
Automate the detection of suspicious transactions and other anomalous behavior faster and alert your customers in a timely fashion to reduce potential financial loss and strengthen customer trust.
Credit risk prediction
Forecast the likelihood of a loan default to maximize risk-adjusted returns.
Extract & analyze data from documents
Automatically extract, process, and analyze data from handwritten and electronic documents for more accurate investigation and faster decision making.
Churn prediction
Predict likelihood of customer churn and improve retention by honing in on likely abandoners and taking remedial actions such as promotional offers.
Demand forecasting
Forecast demand metrics faster and more accurately for timely decision making to help meet customer expectations and reduce inventory carrying costs and waste.
Personalized recommendations
Deliver customized, unique experiences to customers to improve customer satisfaction and grow your business rapidly.
Getting Started
Use case |
Solution |
Get Started |
| Corporate Credit Rating Prediction | Multimodal (long text and tabular) ML for quality credit predictions | GitHub » |
Predictive maintenance |
Predictive maintenance for vehicle fleets |
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Predictive maintenance for manufacturing |
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Computer vision |
Product defect detection in images |
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Autonomous driving |
Visual perception with active learning for autonomous vehicles |
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Fraud detection |
Detect malicious users and transactions |
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Fraud detection in financial transactions using deep graph library |
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Credit risk prediction |
Explain credit decisions |
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Extract & analyze data from documents |
Differential privacy for sentiment classification |
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Document summarization, entity, and relationship extraction |
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Handwriting recognition using Amazon SageMaker |
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Filling in missing values in tabular records |
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Churn prediction |
Churn prediction with text |
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Demand forecasting |
Demand forecasting with deep learning |
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Personalized recommendations |
Entity resolution in identity graphs with deep graph library |
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| Purchase modeling |
Customers
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Mission Automate
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“Thanks to Amazon SageMaker JumpStart, we are able to launch ML solutions within days to fulfill machine learning prediction needs faster and more reliably.”
Alex Panait, CEO – Mission Automate
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MyCase
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“Thanks to Amazon SageMaker JumpStart, we can have better starting points which makes it so that we can deploy a ML solution for our own use cases in 4-6 weeks instead of 3-4 months.”
Gus Nguyen, Software Engineer – MyCase
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MyCase
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“With Amazon SageMaker JumpStart, we can build ML applications such as automatic anomaly detection or object classification faster and launch solutions from proof of concept to production within days.”
Milos Hanzel, Platform Architect – Pivotree
Resources
Detecting fraud in heterogeneous networks using Amazon SageMaker and Deep Graph Library
Scaling your AI-powered Battlesnake with distributed reinforcement learning in Amazon SageMaker
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