Solution Templates

SageMaker JumpStart provides one-click, end-to-end solutions for many common machine learning use cases. Explore the following use cases for more information on available solution templates.

Choose the solution template that best fits your use case from the JumpStart landing page. When you choose a solution template, JumpStart opens a new tab showing a description of the solution and a Launch button. When you select Launch, JumpStart creates all of the resources that you need to run the solution, including training and model hosting instances. For more information on launching a JumpStart solution, see Launch a Solution.

After launching the solution, you can explore solution features and any generated artifacts in JumpStart. Use the Launched JumpStart assets menu to find your solution. In your solution's tab, select Open Notebook to use provided notebooks and explore the solution’s features. When artifacts are generated during launch or after running the provided notebooks, they're listed in the Generated Artifacts table. You can delete individual artifacts with the trash icon ( ). You can delete all of the solution’s resources by choosing Delete solution resources.

Demand forecasting

Demand forecasting uses historical time series data in order to make future estimations in relation to customer demand over a specific period and streamline the supply-demand decision-making process across businesses.

Demand forecasting use cases include predicting ticket sales in the transportation industry, stock prices, number of hospital visits, number of customer representatives to hire for multiple locations in the next month, product sales across multiple regions in the next quarter, cloud server usage for the next day for a video streaming service, electricity consumption for multiple regions over the next week, number of IoT devices and sensors such as energy consumption, and more.

Time series data is categorized as univariate and multi-variate. For example, the total electricity consumption for a single household is a univariate time series over a period of time. When multiple univariate time series are stacked on each other, it’s called a multi-variate time series. For example, the total electricity consumption of 10 different (but correlated) households in a single neighborhood make up a multi-variate time series dataset.

Credit rating prediction

Use JumpStart's credit rating prediction solutions to predict corporate credit ratings or to explain credit prediction decisions made by machine learning models. Compared to traditional credit rating modeling methods, machine learning models can automate and improve the accuracy of credit prediction.

Fraud detection

Many businesses lose billions annually to fraud. Machine learning based fraud detection models can help systematically identify likely fraudulent activities from a tremendous amount of data. The following solutions use transaction and user identity datasets to identify fraudulent transactions.

Computer vision

With the rise of business use cases such as autonomous vehicles, smart video surveillance, healthcare monitoring and various object counting tasks, fast and accurate object detection systems are rising in demand. These systems involve not only recognizing and classifying every object in an image, but localizing each one by drawing the appropriate bounding box around it. In the last decade, the rapid advances of deep learning techniques greatly accelerated the momentum of object detection.

Extract and analyze data from documents

JumpStart provides solutions for you to uncover valuable insights and connections in business-critical documents. Use cases include text classification, document summarization, handwriting recognition, relationship extraction, question and answering, and filling in missing values in tabular records.

Predictive maintenance

Predictive maintenance aims to optimize the balance between corrective and preventative maintenance by facilitating the timely replacement of components. The following solutions use sensor data from industrial assets to predict machine failures, unplanned downtime, and repair costs.

Churn prediction

Customer churn, or rate of attrition, is a costly problem faced by a wide range of companies. In an effort to reduce churn, companies can identify customers that are likely to leave their service in order to focus their efforts on customer retention. Use a JumpStart churn prediction solution to analyze data sources such as user behavior and customer support chat logs to identify customers that are at a high risk of cancelling a subscription or service.

Personalized recommendations

You can use JumpStart solutions to analyze customer identity graphs or user sessions to better understand and predict customer behavior. Use the following solutions for personalized recommendations to model customer identity across multiple devices, to determine the likelihood of a customer making a purchase, or to create a custom movie recommender based on past customer behavior.

Reinforcement learning

Reinforcement learning (RL) is a type of learning that is based on interaction with the environment. This type of learning is used by an agent that must learn behavior through trial-and-error interactions with a dynamic environment in which the goal is to maximize the long-term rewards that the agent receives as a result of its actions. Rewards are maximized by trading off exploring actions that have uncertain rewards with exploiting actions that have known rewards.

RL is well-suited for solving large, complex problems, such as supply chain management, HVAC systems, industrial robotics, game artificial intelligence, dialog systems, and autonomous vehicles.

Healthcare and life sciences

Clinicians and researchers can use JumpStart solutions to analyze medical imagery, genomic information, and clinical health records.

Financial pricing

Many businesses dynamically adjust pricing on a regular basis in order to maximize their returns. Use the following JumpStart solutions for price optimization, dynamic pricing, option pricing, or portfolio optimization use cases.

Causal inference

Researchers can use machine learning models such as Bayesian networks to represent causal dependencies and draw causal conclusions based on data. Use the following JumpStart solution to understand the causal relationship between Nitrogen-based fertilizer application and corn crop yields.