Catalyst QuickML is a no-code machine learning pipeline builder service that lets you implement a host of pre-built ML algorithms, operations, and data preprocessing techniques, and connect with datasets to build and publish ML models. After you publish the data pipeline and ML pipeline, you can access the models you create with authenticated endpoints.
Execute QuickML Endpoint
The code snippet given below allows you to pass input data to a published QuickML endpoint, and predict the outcome based on the ML model’s processing. The output returns the prediction of the values of the target column that is defined while creating the ML pipeline.
The input data to the model’s endpoint are passed as key-value pairs through a hashmap. The endpoint_key mentioned below is the unique ID of the endpoint published for the ML model configured in your project. The endpoint key and the input data are passed to the predict() method for execution.
import java.util.HashMap; import com.zc.component.quickml.ZCQuickML; import com.zc.component.quickml.ZCQuickMLDetail; // input data HashMap<String, String> map= new HashMap<>(); //Give column name and value as per your data set map.put("column_name1", "value1"); map.put("column_name2", "value2"); map.put("column_name3", "value3"); String EndPointKey = "c8c7b4bfd8fdf4f29b9499fjhd9d03f5b2a79cd4792b302e26934a3db984214fd4cfc8049bba7acb15fe73eac0d15af59"; // End point key copied from catalyst UI. // create a quickml instance ZCQuickML quickMlInstance = ZCQuickML.getInstance(); // execute method ZCQuickMLDetail result = quickMlInstance.predict(EndPointKey, map); System.out.println(result.getStatus()); // return the result status System.out.print(result.getResult()); // return the result data
Last Updated 2023-10-12 19:23:27 +0530 +0530
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