Overview
IntelliBuddies is a full-stack RPA platform. In addition to activities provided to automate repeated rule-based tasks, IntelliBuddies also provides AI/ML Activities so that you can infuse AI into your automation, thus enabling your buddies to think, decide, and learn like humans. It includes many activities under the Machine Learning category, as listed below.
| Activity | Description |
|---|---|
| Classification Model Generator | Generates a Classification Model with the specified name using the specified dataset and algorithm. |
| Classification Model Predictor | Invokes the specified Classification Model on the input data and returns the response from the model. |
| Clustering Model Generator | Generates a Clustering Model with the specified name using the specified dataset and the specified algorithm. |
| Clustering Model Predictor | Invokes the specified Clustering Model on the input data and returns back the response from the model. |
| Extract Meta Data | Extracts the metadata from the content provided based on the specified Content Type. |
| Match Mail Type | Matches the email type with the specified type based on the email content provided. It returns true or false based on the match result. |
| Number Parser | Parses the string expected to hold the Numeric value and give you back the good Numeric value if there are any parse errors. |
| Regression Model Generator | Generates a Regression Model with the specified name using the specified dataset and algorithm. |
| Regression Model Predictor | Invokes the specified Regression Model on the input data and returns the response from the model. |
| Date Parser | Parses the string expected to hold the Date value and give you back the proper Date value if there were any parse errors. |
| Detect Language | Detects the Language of the provided text using IntelliBuddies AI Cognitive Services. |
| Detect Objects | Detects the objects inside the given image using IntelliBuddies AI Cognitive Services. |
| Document Classifier | Predicts the type of document based on its content. |
| Get Key Phrases | Extracts the key phrases of the provided text using IntelliBuddies AI Cognitive Services. |
| Get Named Entities | Detects named entities of the provided text using IntelliBuddies AI Cognitive Services. |
| Get PII Entities | Detects the provided text's Personally Identifiable Information(PII) entities using IntelliBuddies AI Cognitive Services. |
| Get Text Summary | Generates the summary of the provided text using IntelliBuddies AI Cognitive Services. |
| Invoice Analyzer | Analyzes and extracts all the metadata from the specified Invoice. |
| Number Parser | Parses the string expected to hold the Numeric value and give you back the good Numeric value if there are any parse errors. |
| Sentiment Classifier | Detects the specified text's sentiment as positive, negative, or neutral. |
| Transcribe Audio | It transcribes the provided Audio into text using IntelliBuddies AI Cognitive Services. |
| Text To Speech | It converts text to Audio and saves it to a specified file using IntelliBuddies AI Cognitive Services. |
| Translate Language | Translates the Language of the provided text using IntelliBuddies AI Cognitive Services. |
Python Context
Our Machine Learning Activities need a Python environment to execute. Hence, it is mandatory to have all our Machine Learning Activities to be embedded inside Python Context activity. The Python Context container activity connects to the Python environment installed on the execution system. It enables the execution of the activities within this container using the Python environment.
PythonObject
For automation to pass and retrieve data to and from Python Context, IntelliBuddies has introduced the PythonObject data type. You can use PythonObject to handle and manipulate Python data. You can convert PythonObject into its corresponding .Net data type using ConvertPythonObject activity.
Example
To understand AI Infused Automation using IntelliBuddies, we have created this example to understand better our Machine Learning Activities.
Download Example
The example has two processes.
InsurancePremiumPredictionModelGenerator
This process will generate a Regression Model using the Random Forest ML algorithm. Generates the model based on the insurance dataset "insurance.csv." Refer: insurance-data
InsurancePremiumPredictor
This process tests the model generated by the previous step. It accepts input data from the user for all the required columns. It then invokes the generated model, passing the input data received. The model's predicted premium is shown in the MessageBox.