AutoML enables you to easily analyse a set of training data and generate predictive analytics on the dataset without requiring you to be involved in the complex ML training process that involves selecting the right ML algorithms to train the model, preprocessing or profiling the data, or managing the models. Catalyst implements the required model training, and automates the entire process for you.

QuickML on the other hand provides you more control in managing ML and data operations, and lets you build, test, deploy, and monitor effective ML models end-to-end. You will be able to perform a host of data preprocessing and transformation operations, pick the ML algorithms for training, and design the pipeline exactly as you need, all with no coding involved.

User data is encrypted and stored safely by adhering to all the security measures applied in Zoho. The data uploaded will not be used to improve the accuracy of any of the QuickML Platform’s algorithms. It is only used to train the intended customer model that the user configures.

Catalyst Projects are intended to provide a clear isolation between data that are being handled. It is not possible to access the data from one project from another.

Data quality score is calculated by internal metrics considering the invalid and missing values present in the dataset.

By using Zoho CRM Bulk APIs, maximum of six hundred thousand/ six lakh records will be fetched into QuickML even if synchronization is configured.

You can edit the synchronization option of Particular dataset by choosing None option in Sync Frequency Dropdown box in dataset details page.

Yes. You can use multiple datasets in a single pipeline by using the Add Dataset stage and configuring the required dataset in it.

Data pipeline is intended to do data pre-processing on the original dataset and reuse the dataset for future pipeline creations. Hence, the data pipeline will only have data operations while the ML pipeline will contain both data and ML operations.

Profile in Dataset details page represents details for whole data but profile in source stage represents details for sampled data from the original dataset.

No. Once the target column of the pipeline is saved, it cannot be changed. We will have to create a new ML pipeline with the new target column to experiment.

Zia features are in-house ML/DL models that are pre-trained using large open-source dataset to solve common use-cases and integrated into builder to enhance data preprocessing capabilities.

All the pipeline executions are queued and handled asynchronously inside QuickML and based on demand the execution will take place.

In certain cases, the operation might be costlier in terms of computation, so there may be a delay. However, the status of the pipeline execution will be updated respectively once the execution succeeds or fails.

Model will be created at the successful execution of the Model pipeline automatically.

Once the models are created, we can view the details associated with that model and pipeline under models module.

While we create an endpoint for a model, QuickML platform automatically enables a REST API endpoint which is to test and verify the model behavior, and is free to use for 1000 invocations.

After verifying the endpoint has to be published to access it in production environment for production grade integrations and charged as per usage.

There are two modes available to integrate QuickML endpoints.

REST API: Zoho OAuth authenticated REST API calls.
QuickML SDKs: External calls can be made using Java, Python and NodeJs Catalyst SDKs.