Artificial Intelligence (AI) Models recognize patterns based on previous data, which in turn can be used to make predictions, decisions, or interpretations. Although AI models are usually built based on various suitable machine learning algorithms, Zoho Creator has simplified the process by combining these algorithms into an intuitive interface that completely hides their complexity. This helps low-code developers utilize machine learning without having to worry about the underlying mechanism.
When applied on Zoho Creator, AI models can optimize business processes by forecasting future outcomes or automating operations that normally need human intervention. For example, a CRM application built on Zoho Creator can use AI to predict if a prospective client can be converted based on present and past data. This insight can be used by the sales team to devise ways to approach the client. Alternatively, a Survey application built on Zoho Creator can use AI to automatically analyze the sentiment expressed in the feedbacks.
Zoho Creator provides support for five common machine-learning models that can work efficiently for business applications. Some of these models are prebuilt, which are readily available to be deployed in applications, whereas others are customizable according to your requirement. Neither require any prior machine learning or coding skills to utilize them. Based on this build-type, the models are classified into:
The following are the model types supported by Zoho Creator:
Category | Model type | Description | Input data type | Usecase |
Ready to use | Extracts the key elements like words and phrases from the input | Text | ||
Detects the attitude of the input statement: positive, negative, or neutral | Text | |||
Extracts all texts in images and convert them into a digital form | File (Image) | |||
Detects predefined elements in the input image | File (Image) |
| ||
Custom | Predict future events or outcomes by analyzing different patterns in the past data | Structured data | ||
Extracts specific texts in images and convert them into a digital form | File (Image) | |||
Detects elements in the input image | File (Image) |
Generally, in order to deploy an AI model, it needs to be built based on suitable ML algorithms, fed with a dataset of appropriate size and quality, trained so it recognizes patterns, and tested to ensure accurate results. For ready-to-use AI models, all these steps are properly implemented and made available in Zoho Creator applications as fields in forms.
However, in some scenarios, AI models need to be tailor-made for a specific requirement. To satisfy this purpose, Zoho Creator's AI Modeler can be used to build custom AI models to get desirable results. This can be viewed as a builder that lets you easily create AI models without directly working with any deep learning concepts.
For example, let's assume when a picture of a printed receipt is uploaded, all of its content needs to be fetched and be inserted in an email template. For this scenario, the ready-to-use OCR model can be used because there is no custom need and it simply extracts all available texts from the picture. Now, consider receipts of the same template will be uploaded every time, and only the IDs of the receipts need to be extracted and populated in the Receipt ID field of the same form. Here, the custom OCR model can be used to serve this purpose as the model needs to be trained for one particular receipt template.
Let's consider a few business situations in which the AI modeler would come in handy.
A delivery partner company called Zylker has built a ticketing application using Zoho Creator. They use this application to track incoming tickets and provide immaculate customer service. Every time they receive a ticket, they'll categorize them and add tags based on the type of query. This makes it easier for the experts in the area to look into and address it.
With Creator's keyword extraction AI model, this process can be automated. For example, if the ticket contains words like 'faulty', 'spoiled', 'damaged', 'defective', and 'refund', the ticket can be auto-tagged as 'Return & Refund'. Similarly, if 'delayed', 'missing', 'rude', and so on appears, the ticket can be tagged as 'Delivery Agent'. This helps assigning the right customer support agent to tickets and drastically reduces the manual labor of categorizing tickets.
Zylker's delivery company recently launched a 10 minute grocery delivery service in a couple of locations. To learn about the reach of this service and to know how customers feel about it, they circulated a survey form built using Zoho Creator.
When there are too many survey results, the sentiment analysis AI model can be used to quickly get an overall insight into the customers' opinion about the new service. For example:
A processed food manufacturing company called Zylker uses a Zoho Creator application to manage their organization process. Their research teams continuously work on new recipes and updates a form with the specifications of the recipes that are approved to be launched as a product. The marketing team has to take it from here and decide upon selling prices based on market research. In this scenario, a custom AI prediction model can make their job easier by suggesting a tentative price based on various influencing factors like market value of similar competitor products, weight, ingredients, and choice of packaging.
Zylker processed food manufacturing company manages their orders using an order management application that is built using Zoho Creator. They supply their products to many buyers in bulk and send out auto-generated sales orders as acknowledgement. All sales orders are maintained in a form along with other essential information like order ID and buyer name.
Although maintaining records of sales orders is an efficient process, doing it manually is monotonous and inefficient. In this scenario, the custom OCR model can be trained to work exclusively on the sales order layout that Zylker issues. Upon upload of Zylker's sales order, layout-specific information like order ID and buyer name can be automatically extracted and maintained in the same form, saving time and manual labor.
An automobile resale company, Zylker buys and sells used vehicles online. They embedded a Zoho Creator form in their website for the sellers to provide information about the vehicle that needs to be sold including its image. Before a proper inspection is done, a custom object detection AI model can be used to tag and categorize the proposals based on the damage level of the vehicle. For example, a vehicle with too many dents can be tagged as 'Severe damage', vehicles with a few dents can be tagged as 'Moderate damage', and so on.
No set up is required to deploy ready-to-use models in your application. However, for custom models , the following procedure needs to be followed before it can be added to your applications as fields in forms.
In the Microservices section, choose the required custom model type to be implemented in your application. A straightforward builder is offered to create the selected AI model with custom intelligence catering to your business needs.
The next step is to upload training data and train the model. Training data is the initial dataset that must be fed to teach the model to identify patterns so it can make predictions or perform a required task. The principle behind a good AI model is collecting sufficient, relevant, and high-quality data to train it. Keep in mind one of the oldest and most used phrases in data science: "garbage in, garbage out". Low quality training data will compromise the accuracy of the model's outcome. Some factors that can ensure the quality of training data are:
Once the dataset is ready, upload it and train the model. Training takes a while based on factors like model size, queued models, etc., however you can navigate away and come back later once the model is trained.
After successful training, the model is practically ready to be deployed. However, it is important to evaluate the model's performance and quality. You can test the model by giving sample inputs; if the test result is not satisfactory, it could be because your model is either underfitting or overfitting.
Your model is underfitting if it performs poorly on the training data. This is because the model is unable to understand the relationship between the input and output variables. Your model is overfitting if it performs well on the training data but produces incorrect outputs on the test data. This is because the model has memorized the data it had seen and is unable to generalize to unseen examples.
In other words, underfitting models learn the variations in the training data rather than the patterns, whereas the overfitting models learn the training data by heart that it wouldn't perform well on any other inputs.
Both overfitting and underfitting are problems that eventually cause poor results on new data and the key to fixing this is to regularize the training data, i.e., the training data should neither have too much variations nor have values that are too close to the expected outcome.
See how you can improve the accuracy of each custom model type:
The next and final step to set up an AI model is to publish it. Until this point, the model is accessible only by the admins. If you are satisfied with your model, make it available for all users by publishing it. This means the model is ready to be implemented in your Zoho Creator applications.
A custom AI model , once configured, can always be edited, renamed, retrained, or deleted. A good AI model must be built, fed with quality data, and deployed. However, it doesn't end there. Retaining the quality of an AI model is a continuous process. Over time, as the business environment and data change, it is inevitable that AI models experience model drift . In other words, the accuracy of model will begin to degrade. For example, a company's sales before covid and after covid are different. An AI model that is built to forecast sales must be trained considering the covid factor too.
For this reason, it's crucial to test and retrain the AI models with updated new data periodically or whenever an evident change in trend occurs. Options to edit, rename, retrain , and delete the model are available in the model details page. This page also shows other details like versions and applications list where the model is deployed.
To implement an AI model in your Zoho Creator applications, use one of the following methods:
Note: For the applications that have environments enabled, you can use custom AI model only if the application has at least one version published in the production environment.
Get started and build your custom AI models using the sample data we've provided in the attachment.
Learn how to use the best tools for sales force automation and better customer engagement from Zoho's implementation specialists.
If you'd like a personalized walk-through of our data preparation tool, please request a demo and we'll be happy to show you how to get the best out of Zoho DataPrep.
You are currently viewing the help pages of Qntrl’s earlier version. Click here to view our latest version—Qntrl 3.0's help articles.