The industry accelerators that are provided by IBM are a set of artifacts that help you address common business issues.
Each industry accelerator is designed to help you solve a specific business problem, whether it's preventing credit card fraud in the banking industry or optimizing the efficiency of your contact center.
All accelerators include a business glossary that consists of terms and categories for data governance. The terms and categories provide meaning to the accelerator and act as the information architecture for the accelerator.
Some accelerators also 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, Juptyer notebooks, models, and R Shiny applications. Use these sample projects as templates for your own data science needs to learn specific techniques, or to demonstrate the capabilities of Watson™ Studio and other AI and analytics services.
A business glossary helps you describe your data with a standard vocabulary. You import categories and business terms as governance artifacts.
- Data stewards and other users who are responsible for creating governance artifacts to govern data.
- A set of business terms to describe data, with logical relationships between terms. See Business terms.
- A category with the same name as the accelerator in which to organize the terms. See Categories.
- The Watson Knowledge Catalog service.
- You must have the Manage categories and Manage governance artifacts permissions. To see which permissions you have, click your user avatar, select Profile and settings, and then view the Permissions page. If you need more permissions, contact your Cloud Pak for Data administrator.
- Process overview
- Each accelerator provides detailed instructions. These general steps provide an overview of the process:
- Import the category.
- Import the business terms.
- Publish the business terms so that they are available in all catalogs and during automated discovery.
- Assign business terms to columns in data sets with one of these methods:
- Add business terms to a data asset in a catalog on the asset's Overview page. See Editing asset properties.
- Some accelerators provide a Jupyter notebook that assigns terms to the data sets included in the sample project.
- Run automated discovery so that business terms are automatically assigned to columns in the discovered data assets. See Discovering assets (Watson Knowledge Catalog).
Sample analytics project
An analytics project contains the assets that you need to build and train the models that are associated with the accelerator. You import the project with data science assets.
- Data scientists or business analysts who analyze data and build models to solve business problems.
- A readme file that provides instructions.
- CSV files that contain the sample data.
- Python 3.6 notebooks to train and score the models and associated Python scripts to prepare and transform the data for modeling. The notebooks include 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.
- All users have permission to create analytics projects.
- All accelerators that have sample projects require the Watson Studio service and one or more of these services:
- To check which services you have on your platform, from the toolbar, click the Services icon. The services that show the Enabled tag are available. If you're missing services, contact your Cloud Pak for Data administrator.
- Process overview
- Each accelerator provides detailed instructions. These steps provide an overview of the process
for sample projects:
- Import the analytics project. See Importing a project.
- Follow the instructions in the README to run the notebooks and perform 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 will 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.
List of accelerators
Find the most up-to-date list of industry accelerators in the Cloud Pak for Data Community.