Overview
Bizagi Cloud applications let you go beyond process execution and use your data to explore fields such as artificial intelligence (predictive analysis), integrate reporting tools of your choice, and create portals to provide a richer user experience.
Artificial Intelligence is an application which lets you explore machine learning capabilities directly in your Bizagi processes, by letting the processes use a predictive analysis service that is based upon the reliable data that you have in a Bizagi Dataset (Business Insights).
Through predictive analysis, you can train models and carry out experiments that rely on renowned machine learning algorithms to determine a given outcome based on stored data with a given degree of certainty.
For such models, you can easily set your Bizagi processes to present a prediction once certain data has been provided. You can use that prediction as a default value, or offer it for user approval.
You can configure all of this without being an expert data scientist.
Bizagi Artificial Intelligence lets you predict categorical or numerical attributes and variables using Artificial Intelligence algorithms so you can use the results in your Bizagi processes or as a basis for making decisions. Some examples of use case statements could be:
•For a credit application process where a customer requests a loan:
Based on credit amount, customer type and credit type, we could predict whether the process requires additional documentation (predicting a categorical variable).
•For a vehicle insurance underwriting process:
Based on insurance amount, customer type and estimated price, we could predict an estimated duration for which to approve the insurance underwriting (predicting a numerical variable).
•For a help desk support process where a customer submits a ticket:
Based on severity, operating system, and ticket type, we could predict whether the ticket is likely to be resolved by first-level support, or whether it will need to be escalated to second or to third-level support (predicting a categorical variable).
Before you start
Artificial Intelligence relies on Bizagi Business Insights as its data provider.
Before moving on, make sure you are familiar with Bizagi Business Insights, and have created a Dataset to use with the Artificial Intelligence application.
For more information about Datasets, refer to Bizagi Business Insights.
Getting started with Bizagi Artificial Intelligence
When you purchase Bizagi Artificial Intelligence, you need to provide the users who are authorized to log in to the BI portal. Send the list of users via Ticket. Once you receive the answer to the ticket your authorized users need to use the Forgot password option to set their passwords.
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As an additional security measure, you can also send in your ticket the list of IP addresses that can access your Bizagi Artificial Intelligence. |
The following steps summarize at high level how you work with Artificial Intelligence:
1. Create an AI model and define its data source.
Set it to connect to a Dataset, that holds relevant data.
2. Explore the results of different experiments, and choose a configuration that provides results that satisfy your use case statement.
You can evaluate possible combinations of input parameters and algorithms.
Once the experiment is satisfactory and final, publish it.
3. Connect your Bizagi processes to consume the predictive analysis service so that each new case can make use of a preliminary prediction for an attribute/value.
Configure the invocation to use a RESTful service and that's it.
Important recommendations
1. Always create models and experiments based on real data (e.g, by using a Dataset from the Production environment).
2. Only publish an experiment when it is satisfactory.
Publishing an experiment allows you to use one service endpoint for testing, and another one for real use (a process's Production environment).
For the service endpoint used in testing, you may define fixed rules so that your AI service returns predictable outputs and you can easily work in all of the process’s different paths.
3. Constantly train the model.
By default, new data stored in the Dataset is not considered by an experiment until you explicitly train the model to re-evaluate the data.
When you do this, make sure to re-publish the experiment so that the same service endpoint being used by processes is updated.
You can update an existing experiment which has been published and is under current use.
4. When an experiment in current use changes its input or output definitions, you need to create a new experiment.
We strongly recommend this as a best practice to maintain the stability of ongoing processes.
Further information
To learn how to get started with Artificial Intelligence, refer to Creating models and experiments.