General
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.
Datasets
Pipelines
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.
Models
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.
Endpoints
While we create an endpoint for a model, QuickML platform automatically enables an 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.
Last Updated 2023-10-08 10:48:45 +0530 +0530
Yes
No
Send your feedback to us