The industry accelerators provided by IBM are a set of end-to-end solutions that you can run as examples or customize them to address common business issues.
Overview of industry accelerators
Each industry accelerator is designed to help you solve a specific business problem, whether it's predicting how customers will respond to a promotion or understanding customer attrition. Browse the accelerators in the Cloud Pak for Data as a Service Resource hub and create the ones you want.
Most accelerators include a Sample project with everything you need to analyze data, build a model, and display results. The sample projects include detailed instructions, data sets, Jupyter notebooks, models, and R Shiny applications. Most accelerators also include business terms for data governance. Use these sample projects as templates for your own data science needs to learn specific techniques, or to demonstrate the capabilities of watsonx.ai Studio and other Cloud Pak for Data as a Service services.
The process of using an industry accelerator is illustrated in this graphic.
Sample project for an accelerator
When you download an accelerator, you get a project that contains the assets that you need to build and train the models that are associated with the accelerator.
Audience
Data scientists or business analysts who analyze data and build models to solve business problems.
Contents
A typical accelerator contains the following:
- A readme file that provides instructions.
- CSV files that contain the sample data.
- Python notebooks to train and score the models and associated Python scripts to prepare and transform the data for modeling. The notebooks create API endpoints to expose the output for the R Shiny application.
- Machine learning models that are designed to help you find answers to the business problems described by the accelerator.
- (Some accelerators) An interactive dashboard to show the results of the model.
Requirements
All accelerators that have sample projects require the watsonx.ai Studio service and one or more of these services:
- watsonx.ai Runtime
- IBM Knowledge Catalog
Process overview
Each accelerator provides detailed instructions. These steps provide an overview of the process for sample projects:
- Go to the sample project in Resource hub, click Create project, select project configurations, and click Create. See Creating a project.
- Follow the instructions in the readme file to run the notebooks and complete other tasks.
Next steps
You can use the sample project as a template by adding your own data and following the same steps to go from data to deployed model. You might need to explore and cleanse your data. Because your data and schema are likely different from the sample data, the patterns that you find in your data might not match the patterns in the sample data. You can use the examples and code to adapt the model for your data and to retrain the model with your data.
To use a sample project as a template for your own data, follow these general steps:
- Add your data to the project.
- If necessary, cleanse or shape your data.
- Update and retrain the model with your data.
Accelerators for financial services
| Industry accelerator | Description |
|---|---|
| Financial Markets Customer Attrition Prediction | Provides a set of sample data science assets and a structured set of business terms that help you to predict which customers will leave. |
| Financial Markets Customer Offer Affinity | Predicts which products the customer is most likely to consider or require next to purchase. |
| Financial Markets Customer Segmentation | Assigns customers to specific groups, based on their lifestyle and behavioral patterns. |
| Financial Markets Customer Life Event Prediction | Use the Financial Markets Customer Life Event Prediction accelerator to set your clients on the path to financial success with relevant offers at the right time. The accelerator includes business terms, a set of sample data science assets, and a sample dashboard to visualize the results. |
| Sales Prediction using Weather Company Data | Optimize your company's sales organization capabilities by training a model to predict sales based on weather. The accelerator includes business terms and categories, a set of sample data science assets, and a sample dashboard to visualize the results. |
| Retail customer retention | Use customer satisfaction surveys to predict customer churn and come up with retention strategies. |
Accelerators for utility and energy sector
| Industry accelerator name | Description |
|---|---|
| Utilities Customer Attrition Prediction | Provides likely causes of customer attrition. |
| Utilities Customer Micro-Segmentation | Assigns customers to specific groups, based on their lifestyle and behavioral patterns. |
| Utilities Demand Response Program Propensity | Which customers should be offered the opportunity to enroll in the Demand Response Program? Use the Utilities Demand Response Program Propensity accelerator to jump-start your analysis. The accelerator includes business terms, a set of sample data science assets, and a sample RStudio dashboard to visualize the results. |
| Utilities Payment Risk Prediction | Use the Utilities Payment Risk Prediction accelerator to proactively engage with customers at risk of missing payments. The accelerator includes business terms, a set of sample data science assets, and a sample dashboard to visualize the results |
Accelerators for other categories
| Industry accelerator name | Description |
|---|---|
| Comments organizer project | allow companies to view comments in a more organized manner and to more easily view customers' specific positive or negative feedback. |
| Effective farming project | Supports effective farming by monitoring crop growth using crop guide and provide timely alert to farmers about weather change, possible development of crop disease, evaporation of fungicide, and efficient use of solar panels (agrivoltaics support). |
| Insurance Loss Estimation using Remote Sensing | Uses remote sensing flooding data to assist with assessing insurance claims. |
| Hospital readmission project | Predicts hospital readmission rate of patients by using patient data. |
| Monitor Amazon SageMaker model using Watson Openscale | This project shows how to use the endpoint generated from Deploy an IBM watsonx.ai Studio Model on Amazon SageMaker to demonstrate how to monitor the AWS deployment with Watson OpenScale. |
Parent topic: Getting started