Documentation
Frequently Asked Question
Hyper-automation is the use of advanced technologies, including artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and other digital tools, to automate complex business processes and tasks. It aims to enhance automation by combining multiple technologies to achieve more sophisticated and comprehensive automation outcomes.
Traditional automation focuses on automating specific, repetitive tasks using rule-based tools. Hyper-automation, on the other hand, integrates multiple advanced technologies to automate end-to-end processes, enabling more intelligent, flexible, and scalable automation solutions. It goes beyond simple task automation to include process discovery, analytics, and optimization.
Implementing hyper-automation offers several benefits, including increased efficiency, reduced operational costs, improved accuracy, enhanced employee productivity, and better decision-making through advanced analytics. It also enables organizations to scale their automation efforts, adapt to changing business needs, and stay competitive in the market.
Common use cases of hyper-automation include customer service automation, supply chain optimization, finance and accounting process automation, HR and employee onboarding, IT operations management, and healthcare process optimization. These applications leverage AI, ML, and RPA to streamline workflows and improve overall process efficiency.
AI plays a crucial role in hyper-automation by providing the intelligence needed to handle complex and dynamic tasks that traditional automation cannot manage. AI technologies, such as machine learning, natural language processing, and computer vision, enable systems to learn from data, make decisions, understand and process human language, and recognize patterns. This allows hyper-automation to automate tasks that require cognitive capabilities, such as data analysis, customer service interactions, and predictive maintenance.
Hyper-automation can significantly impact the workforce by transforming job roles and responsibilities. While it can lead to the displacement of certain repetitive and manual tasks, it also creates opportunities for employees to focus on more strategic, creative, and value-added activities. Organizations can upskill and reskill their workforce to work alongside automated systems, leveraging human skills in areas such as problem-solving, innovation, and customer engagement. Additionally, hyper-automation can enhance job satisfaction by reducing the burden of mundane tasks and allowing employees to engage in more meaningful work.
- Once logged in, locate the left navigation bar.
- Look for the “Connections” option in the navigation menu and click on it.
- At the top right corner of the connection section, you’ll find a search bar to find any previous connections if needed.
- To create a new connection, click on the “Add Connection” button.
- Click on the "AWS Connection" option.
- Enter your Access Key ID and Secret Access Key in the designated fields.
- Click on the validation button to validate the connection.
- After successful validation, click the “Submit” button to save the AWS connection.
- Choose the “Azure Connection” option.
- Specify the required scope options such as reading and writing browser site lists, viewing users’ email addresses, and accessing user files.
- After selecting the required scopes, click “Submit” to save.
- Enter your Microsoft user ID and password in the Microsoft login window that appears.
- Choose the “Google Connection” option.
- Select the desired functionality (e.g., Firebase Cloud Messaging API v1, Google Drive, Google Calendar).
- Select the necessary permissions for the chosen functionality.
- Click on “Submit” to save.
- Log in to your Google account when prompted.
- Choose the “MySQL” option.
- Provide a connection name for the MySQL connection.
- Enter the hostname, username, password, and port number of the MySQL server.
- Select the specific database within the MySQL server that you want to connect to.
- Click “Submit” to save the connection details.
- Choose the “MailChimp” option.
- Enter a connection name for the MailChimp connection.
- Click “Submit” to proceed to the MailChimp login interface.
- Input your MailChimp user ID or email address and password in the login window.
- Click on the “Login” or “Sign In” button to authenticate your account and complete the setup.
AIRA models require input data in the form of structured datasets, such as CSV or Excel files. This data serves as the foundation for training the model and generating predictions. Users can upload these datasets during the model creation process.
To start training an AIRA model, first, navigate to the "Model" section in the AIRA platform. After uploading your dataset, identify and select the target variable and features for the model. Click the "Start Training" button to begin the training process, where the model will learn patterns from the input data.
Once an AIRA model is trained, you can use it to make predictions by inputting new or unseen data. You can either manually fill in feature values or upload a new dataset for batch predictions. The model will process the input and provide predictive results.
After satisfactory performance during the prediction phase, an AIRA model can be deployed for operational use. To deploy, provide a name for the model and confirm the deployment process within the AIRA platform. The deployed model can then be integrated into various workflows and applications.
To upload an existing model, create a zip file containing the model file (e.g., a pickle file) and a main.py script. In the AIRA platform, navigate to the model upload section, provide a name and description for the model, and upload the zip file. Define the model inputs and outputs before deploying it.
The main.py script serves as the entry point for running your model within AIRA. It should:
- Import necessary dependencies.
- Define a prediction function to load the model and make predictions.
- Create an entry point function to handle command-line arguments and call the prediction function.
- Include a conditional statement to ensure the script runs correctly when executed directly. Package this script with your model file into a zip archive for uploading to AIRA.
AIRA IDP is a module designed to automate the extraction of data from various types of documents, enhancing the efficiency and accuracy of document-related tasks within your workflow.
By automating data extraction, IDP reduces manual efforts, minimizes errors, and speeds up document processing, leading to increased productivity and operational efficiency.
IDP supports various document formats, including JPG, JPEG, PNG, and PDF.
Yes, IDP is equipped to handle a variety of document types, including complex documents such as invoices and forms.
Scroll down the left navigation panel in AIRA and click on the “IDP” section to access the Learning Instance window.
To create a new instance:
- Click on “Add New Instance” in the Learning Instance window.
- Assign a unique name in the “Learning Instance Name” section.
- Upload files by dragging and dropping or browsing in the “Drop File” section.
- Click on “Create” to initiate the instance creation process.
The guide emphasizes checking hardware and software requirements outlined in the installation guide to ensure compatibility with the AIRA Enterprise application.
You can verify the integrity of the downloaded package by checking its size or using any provided checksums, if available, before proceeding with the installation.
Setting the NGINX user as the owner ensures proper permissions for serving AIRA content via the NGINX web server and enhances security by restricting access to authorized users.
After installing npm dependencies and PM2 globally, you can start the Node server with node server.js and register the app in PM2 using pm2 start "node server.js" --name "app_name".
The orchestration engine manages workflows, automates tasks, and coordinates processes within the AIRA application, ensuring efficient execution of business logic and workflows.
You can create an NGINX configuration file (aira.conf), specify SSL certificate paths, set up proxy_pass directives for routing requests to the Node.js server, and restart NGINX to apply the changes.