# End-to-end JumpStart solution templates
**Important**
As of November 30, 2023, the previous Amazon SageMaker Studio experience is now named Amazon SageMaker Studio Classic. The following section is specific to using the Studio Classic application. For information about using the updated Studio experience, see [Amazon SageMaker Studio](studio-updated.md).
Studio Classic is still maintained for existing workloads but is no longer available for onboarding. You can only stop or delete existing Studio Classic applications and cannot create new ones. We recommend that you [migrate your workload to the new Studio experience](studio-updated-migrate.md).
**Note**
JumpStart Solutions are only available in Studio Classic.
SageMaker JumpStart provides one-click, end-to-end solutions that are designed to address common machine learning use cases. They use proven algorithms for their domains and provide a complete workflow which typically includes data processing, model training, deployment, inference, and monitoring. Explore the following use cases for more information on available solution templates.
+ [Demand forecasting](#jumpstart-solutions-demand-forecasting)
+ [Credit rating prediction](#jumpstart-solutions-credit-prediction)
+ [Fraud detection](#jumpstart-solutions-fraud-detection)
+ [Computer vision](#jumpstart-solutions-computer-vision)
+ [Extract and analyze data from documents](#jumpstart-solutions-documents)
+ [Predictive maintenance](#jumpstart-solutions-predictive-maintenance)
+ [Churn prediction](#jumpstart-solutions-churn-prediction)
+ [Personalized recommendations](#jumpstart-solutions-recommendations)
+ [Reinforcement learning](#jumpstart-solutions-reinforcement-learning)
+ [Healthcare and life sciences](#jumpstart-solutions-healthcare-life-sciences)
+ [Financial pricing](#jumpstart-solutions-financial-pricing)
+ [Causal inference](#jumpstart-solutions-causal-inference)
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](jumpstart-solutions-launch.md).
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 (![\[The trash icon for JumpStart.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/jumpstart/jumpstart-trash.png)). 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.
| Solution name | Description | Get started |
| --- | --- | --- |
| Demand forecasting | Demand forecasting for multivariate time series data using three state-of-the-art time series forecasting algorithms: [LSTNet](https://ts.gluon.ai/stable/api/gluonts/gluonts.mx.model.lstnet.html), [Prophet](https://facebook.github.io/prophet/), and [SageMaker AI DeepAR](https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html). | [GitHub »](https://github.com/awslabs/sagemaker-deep-demand-forecast) |
## 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.
| Solution name | Description | Get started |
| --- | --- | --- |
| Corporate credit rating prediction | Multimodal (long text and tabular) machine learning for quality credit predictions using AWS [AutoGluon Tabular](https://auto.gluon.ai/scoredebugweight/tutorials/tabular_prediction/index.html). | [GitHub »](https://github.com/awslabs/sagemaker-corporate-credit-rating) |
| Graph-based credit scoring | Predict corporate credit ratings using tabular data and a corporate network by training a [Graph Neural Network GraphSAGE](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf) and AWS [AutoGluon Tabular](https://auto.gluon.ai/scoredebugweight/tutorials/tabular_prediction/index.html) model. | Find in Amazon SageMaker Studio Classic. |
| Explain credit decisions | Predict credit default in credit applications and provide explanations using [LightGBM](https://lightgbm.readthedocs.io/en/latest/) and [SHAP (SHapley Additive exPlanations)](https://shap.readthedocs.io/en/latest/index.html). | [GitHub »](https://github.com/awslabs/sagemaker-explaining-credit-decisions) |
## 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.
| Solution name | Description | Get started |
| --- | --- | --- |
| Detect malicious users and transactions | Automatically detect potentially fraudulent activity in transactions using [SageMaker AI XGBoost](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html) with the over-sampling technique [Synthetic Minority Over-sampling](https://arxiv.org/abs/1106.1813) (SMOTE). | [GitHub »](https://github.com/awslabs/fraud-detection-using-machine-learning) |
| Fraud detection in financial transactions using deep graph library | Detect fraud in financial transactions by training a [graph convolutional network](https://arxiv.org/pdf/1703.06103.pdf) with the [deep graph library](https://www.dgl.ai/) and a [SageMaker AI XGBoost](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html) model. | [GitHub »](https://github.com/awslabs/sagemaker-graph-fraud-detection) |
| Financial payment classification | Classify financial payments based on transaction information using [SageMaker AI XGBoost](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html). Use this solution template as an intermediate step in fraud detection, personalization, or anomaly detection. | Find in Amazon SageMaker Studio Classic. |
## 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.
| Solution name | Description | Get started |
| --- | --- | --- |
| Visual product defect detection | Identify defective regions in product images either by training an [object detection model from scratch](https://ieeexplore.ieee.org/document/8709818) or fine-tuning pretrained SageMaker AI models. | [GitHub »](https://github.com/awslabs/sagemaker-defect-detection) |
| Handwriting recognition | Recognize handwritten text in images by training an [object detection model](https://mxnet.apache.org/versions/1.0.0/api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.resnet34_v1) and [handwriting recognition model](https://arxiv.org/abs/1910.00663). Label your own data using [SageMaker Ground Truth](https://aws.amazon.com/sagemaker/data-labeling/). | [GitHub »](https://github.com/awslabs/sagemaker-handwritten-text-recognition) |
| Object detection for bird species | Identify birds species in a scene using a [SageMaker AI object detection model](https://docs.aws.amazon.com/sagemaker/latest/dg/object-detection.html). | Find in Amazon SageMaker Studio Classic. |
## 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.
| Solution name | Description | Get started |
| --- | --- | --- |
| Privacy for sentiment classification | [Anonymize text ](https://www.amazon.science/blog/preserving-privacy-in-analyses-of-textual-data) to better preserve user privacy in sentiment classification. | [GitHub »](https://github.com/awslabs/sagemaker-privacy-for-nlp) |
| Document understanding | Document summarization, entity, and relationship extraction using the [transformers](https://huggingface.co/docs/transformers/index) library in PyTorch. | [GitHub »](https://github.com/awslabs/sagemaker-document-understanding) |
| Handwriting recognition | Recognize handwritten text in images by training an [object detection model](https://mxnet.apache.org/versions/1.0.0/api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.resnet34_v1) and [handwriting recognition model](https://arxiv.org/abs/1910.00663). Label your own data using [SageMaker Ground Truth](https://aws.amazon.com/sagemaker/data-labeling/). | [GitHub »](https://github.com/awslabs/sagemaker-handwritten-text-recognition) |
| Filling in missing values in tabular records | Fill missing values in tabular records by training a [SageMaker Autopilot](https://aws.amazon.com/sagemaker/autopilot/) model. | [GitHub »](https://github.com/awslabs/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.
| Solution name | Description | Get started |
| --- | --- | --- |
| Predictive maintenance for vehicle fleets | Predict vehicle fleet failures using vehicle sensor and maintenance information with a convolutional neural network model. | [GitHub »](https://github.com/awslabs/aws-fleet-predictive-maintenance/) |
| Predictive maintenance for manufacturing | Predict the remaining useful life for each sensor by training a [stacked Bidirectional LSTM neural network](https://arxiv.org/pdf/1801.02143.pdf) model using historical sensor readings. | [GitHub »](https://github.com/awslabs/predictive-maintenance-using-machine-learning) |
## 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.
| Solution name | Description | Get started |
| --- | --- | --- |
| Churn prediction with text | Predict churn using numerical, categorical, and textual features with [BERT encoder](https://huggingface.co/) and [RandomForestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html). | [GitHub »](https://github.com/awslabs/sagemaker-churn-prediction-text) |
| Churn prediction for mobile phone customers | Identify unhappy mobile phone customers using [SageMaker AI XGBoost](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html). | Find in Amazon SageMaker Studio Classic. |
## 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.
| Solution name | Description | Get started |
| --- | --- | --- |
| Entity resolution in identity graphs with deep graph library | Perform cross-device entity linking for online advertising by training a [graph convolutional network](https://arxiv.org/pdf/1703.06103.pdf) with [deep graph library](https://www.dgl.ai/). | [GitHub »](https://github.com/awslabs/sagemaker-graph-entity-resolution) |
| Purchase modeling | Predict whether a customer will make a purchase by training a [SageMaker AI XGBoost](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html) model. | [GitHub »](https://github.com/awslabs/sagemaker-purchase-modelling) |
| Customized recommender system | Train and deploy a custom recommender system that generates movie suggestions for a customer based on past behavior using Neural Collaborative Filtering in SageMaker AI. | Find in Amazon SageMaker Studio Classic. |
## 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.
| Solution name | Description | Get started |
| --- | --- | --- |
| Reinforcement learning for Battlesnake AI competitions | Provide a reinforcement learning workflow for training and inference with the [BattleSnake](https://play.battlesnake.com/) AI competitions. | [GitHub »](https://github.com/awslabs/sagemaker-battlesnake-ai) |
| Distributed reinforcement learning for Procgen challenge | Distributed reinforcement learning starter kit for [NeurIPS 2020 Procgen](https://www.aicrowd.com/challenges/neurips-2020-procgen-competition) Reinforcement learning challenge. | [GitHub »](https://github.com/aws-samples/sagemaker-rl-procgen-ray) |
## Healthcare and life sciences
Clinicians and researchers can use JumpStart solutions to analyze medical imagery, genomic information, and clinical health records.
| Solution name | Description | Get started |
| --- | --- | --- |
| Lung cancer survival prediction | Predict non-small cell lung cancer patient survival status with 3-dimensional lung computerized tomography (CT) scans, genomic data, and clinical health records using [SageMaker AI XGBoost](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html). | [GitHub »](https://github.com/aws-samples/machine-learning-pipelines-for-multimodal-health-data/tree/sagemaker-soln-lcsp) |
## 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.
| Solution name | Description | Get started |
| --- | --- | --- |
| Price optimization | Estimate price elasticity using Double Machine Learning (ML) for causal inference and the [Prophet](https://facebook.github.io/prophet/) forecasting procedure. Use these estimates to optimize daily prices. | Find in Amazon SageMaker Studio Classic. |
## 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.
| Solution name | Description | Get started |
| --- | --- | --- |
| Crop yield counterfactuals | Generate a counterfactual analysis of corn response to nitrogen. This solution learns the crop phenology cycle in its entirety using multi-spectral satellite imagery and [ground-level observations](https://www.sciencedirect.com/science/article/pii/S2352340921010283#tbl0001). | Find in Amazon SageMaker Studio Classic. |
# Launch a Solution
**Important**
As of November 30, 2023, the previous Amazon SageMaker Studio experience is now named Amazon SageMaker Studio Classic. The following section is specific to using the Studio Classic application. For information about using the updated Studio experience, see [Amazon SageMaker Studio](studio-updated.md).
Studio Classic is still maintained for existing workloads but is no longer available for onboarding. You can only stop or delete existing Studio Classic applications and cannot create new ones. We recommend that you [migrate your workload to the new Studio experience](studio-updated-migrate.md).
**Note**
JumpStart Solutions are only available in Studio Classic.
First, choose a solution through the SageMaker JumpStart landing page in the Amazon SageMaker Studio Classic UI. For information on the onboarding steps to sign in to Amazon SageMaker Studio Classic, see [Onboard to Amazon SageMaker AI domain](https://docs.aws.amazon.com/sagemaker/latest/dg/gs-studio-onboard.html). For details on getting to the SageMaker JumpStart landing page, see [Open and use JumpStart in Studio Classic](studio-jumpstart.md#jumpstart-open-use).
After you choose a solution, a solution's tab opens showing a description of the solution and a `Launch` button. To launch a solution, select `Launch` in the **Launch Solution** section. JumpStart then creates all of the resources needed to run the solution. This includes training and model hosting instances.
## Advanced parameters
The solution that you choose may have advanced parameters that you can select. Choose **Advanced Parameters** to specify the AWS Identity and Access Management role for the solution.
Solutions are able to launch resources across 9 AWS services that interact with each other. For the solution to work as expected, newly created components from one service must be able to act on newly created components from another service. We recommend that you use the default IAM role to ensure that all needed permissions are added. For more information about IAM roles, see [AWS Identity and Access Management for Amazon SageMaker AI](security-iam.md).
**Default IAM role**
If you select this option, the default IAM roles that are required by this solution are used. Each solution requires different resources. The following list describes the default roles that are used for the solutions based on the service needed. For a description of the permissions required for each service, see [AWS Managed Policies for SageMaker Projects and JumpStart](security-iam-awsmanpol-sc.md).
+ **API Gateway** – AmazonSageMakerServiceCatalogProductsApiGatewayRole
+ **CloudFormation** – AmazonSageMakerServiceCatalogProductsCloudformationRole
+ **CodeBuild** – AmazonSageMakerServiceCatalogProductsCodeBuildRole
+ **CodePipeline** – AmazonSageMakerServiceCatalogProductsCodePipelineRole
+ **Events** – AmazonSageMakerServiceCatalogProductsEventsRole
+ **Firehose** – AmazonSageMakerServiceCatalogProductsFirehoseRole
+ **Glue** – AmazonSageMakerServiceCatalogProductsGlueRole
+ **Lambda** – AmazonSageMakerServiceCatalogProductsLambdaRole
+ **SageMaker AI** – AmazonSageMakerServiceCatalogProductsExecutionRole
If you are using a new SageMaker AI domain with JumpStart project templates enabled, these roles are automatically created in your account.
If you are using an existing SageMaker AI domain, these roles may not exist in your account. If this is the case, you will receive the following error when launching the solution.
```
Unable to locate the updated roles required to launch this solution, a general role '/service-role/AmazonSageMakerServiceCatalogProductsUseRole' will be used. Please update your studio domain to generate these roles.
```
You can still launch a solution without the needed role, but the legacy default role `AmazonSageMakerServiceCatalogProductsUseRole` is used in place of the needed role. The legacy default role has trust relationships with all of the services that JumpStart solutions need to interact with. For the best security, we recommend that you update your domain to have the newly created default roles for each AWS service.
If you have already onboarded to a SageMaker AI domain, you can update your domain to generate the default roles using the following procedure.
1. Open the Amazon SageMaker AI console at [https://console.aws.amazon.com/sagemaker/](https://console.aws.amazon.com/sagemaker/).
1. Choose ** Control Panel ** at the top left of the page.
1. From the **domain** page, choose the **Settings** icon (![\[Black square icon representing a placeholder or empty image.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/icons/Settings_squid.png)) to edit the domain settings.
1. On **General Settings** choose **Next**.
1. Under **SageMaker Projects and JumpStart**, select **Enable Amazon SageMaker project templates and Amazon SageMaker JumpStart for this account ** and **Enable Amazon SageMaker project templates and Amazon SageMaker JumpStart for Studio Classic users**, choose **Next**.
1. Select **Submit**.
You should be able to see the default roles listed in **Projects - Amazon SageMaker project templates enabled for this account** under the **Apps - Studio** tab.
**Find IAM role**
If you select this option, you must select an existing IAM role from the dropdown list for each of the required services. The selected role must have at least the minimum permissions required for the corresponding service. For a description of the permissions required for each service, see [AWS Managed Policies for SageMaker Projects and JumpStart](security-iam-awsmanpol-sc.md).
**Input IAM role**
If you select this option, you must manually enter the ARN for an existing IAM role. The selected role must have at least the minimum permissions required for the corresponding service. For a description of the permissions required for each service, see [AWS Managed Policies for SageMaker Projects and JumpStart](security-iam-awsmanpol-sc.md).