Quickstart
Ingest sample data, train and deploy a model, then request your first prediction and explanation.
Use this quickstart to complete one end-to-end workflow with the REST API.
You will upload sample data to Amazon S3, ingest it, onboard it, train a model, deploy it, and query the deployed model for a prediction and explanation.
Before you start
This quickstart assumes that you will:
- download the sample dataset;
- upload it to an Amazon S3 bucket you control; and
- use that S3 location as the source data for the workflow.
You also need permission to:
- create account API credentials; and
- grant the platform read-only access to your S3 bucket.
Organisations and accounts are currently created by Umnai during onboarding. Each account uses storage that is either provisioned by Umnai or provided by the customer.
If your account uses provided storage, complete the Account storage setup before starting. Account storage has a different role from the read-only source storage used in this quickstart. The two roles can use the same S3 bucket, but dedicated buckets are strongly recommended.
Several steps start asynchronous jobs. After you start a job, retrieve its status and continue only when it reaches the status shown in the step.
As you go, save the IDs returned by the API. Later steps use those IDs in request bodies, path parameters, or headers.
1. Authenticate
Authenticate once at the start of the workflow.
Create account API credentials in the dashboard.
Create API credentials
Store the generated client_id and client_secret securely.
Response
{"access_token": "eyJhbGaiOiJSUzI1NiIsInR5cCI6IkpXVCIsImtpZCx6Ijg5YzlmMzdhIn0.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.WTbu-NBi9NSxn9y4tusGJIentDWkDWF2uwcGwp3Ms_2e7yxyqJLSjYXgLjj3oZUs-oVbTJaRmFDPK7B5x8eiyUoD7WxhkcOBTwzFMkcQMkVEVCxCO_zeI_xVPz1auxiKmfNEMcToYMpxkiX401b5Oq_59vESpZPvWhsrV-eLxnADsEV8B2hGEmemAcb_64JJIHipyDfpclpznMzirE6KEJ6AEMlE3BedKV2g0Zb_jsBsQNxY-ZH-EgcGYSc8ft8JlhQZCvva32YD8Z2eJV3BIycGsEs9vnMd1RXiEL3ZrJm7T9S_fzKkIQ5d2Qarg9PAdNBOIPYFsb2Y2klkvs-GsQ","token_type": "Bearer","expires_in": 2592000}
Save the returned access_token as ACCESS_TOKEN.
Use it in the Authorization header for each API request:
Authorization: Bearer ACCESS_TOKEN
2. Connect source data
Grant the platform read-only access to the S3 bucket that contains your source data.
-
In the AWS Console, open S3. Ensure that the selected AWS Region matches the region agreed with Umnai during onboarding.
-
Select the bucket that contains your source data and open the Permissions tab.
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Under Bucket policy, select Edit, then paste the following policy.
-
Obtain the Umnai platform AWS account ID for your environment from Umnai support.
-
Replace
<ACCOUNT-ID>with the account ID provided to you and<YOUR-BUCKET-NAME>with your bucket’s name:{"Version": "2012-10-17","Statement": [{"Sid": "AllowSourceObjectRead","Effect": "Allow","Principal": {"AWS": "arn:aws:iam::<ACCOUNT-ID>:root"},"Action": ["s3:GetObject"],"Resource": "arn:aws:s3:::<YOUR-BUCKET-NAME>/*"},{"Sid": "AllowSourceBucketList","Effect": "Allow","Principal": {"AWS": "arn:aws:iam::<ACCOUNT-ID>:root"},"Action": ["s3:ListBucket"],"Resource": "arn:aws:s3:::<YOUR-BUCKET-NAME>"}]} -
Select Save changes. You should see confirmation that the bucket policy was updated.
Create a storage connector for the bucket.
Response
{"id": "02910f90b30a5b94ad505efe83280dca","name": "Sample Storage Connector","bucket_name": "sample-storage-connector-bucket","account": {"id": "e268443e43d93dab7ebef303bbe9642f","name": "Sample Account"},"created_at": "2026-03-12T13:55:14.315928Z","created_by": {"id": "dad46b2058752ffde5eaf02f1eb6abd5","name": "Sample User"}}
Save the returned id as CONNECTOR_ID.
3. Create processing resources
Create the processing compute configuration used by ingestion and onboarding jobs.
Response
{"id": "351df95006b1350e597762a1d58e9319","name": "Sample Compute Configuration","machine_type": "ml.m5.4xlarge","compute_type": "PROCESSING","account": {"id": "e268443e43d93dab7ebef303bbe9642f","name": "Sample Account"},"created_at": "2026-03-25T15:23:50.965824Z","created_by": {"id": "dad46b2058752ffde5eaf02f1eb6abd5","name": "Sample User"}}
Save the returned id as PROCESSING_COMPUTE_CONFIGURATION_ID.
Create a data schema that describes the source data.
curl -X POST https://api.hi.umnai.com/data-schemas \-H "Authorization: Bearer <token>" \-H "Content-Type: application/json" \-d '{"name": "Sample Schema","fields": [{"name": "age","data_type": {"data_type": "NUMBER","format": "INT64"},"designation": "FEATURE","semantic_type": "CONTINUOUS"},{"name": "occupation","data_type": {"data_type": "STRING"},"designation": "FEATURE","semantic_type": "CATEGORICAL"},{"name": "income","data_type": {"data_type": "NUMBER","format": "INT64"},"designation": "TARGET","semantic_type": "CATEGORICAL"}]}'
Response
{"id": "22f2c353201d472fbfc7c856eaecaccd","account": {"id": "cf5b093e7f65480e98107feb78649222","name": "Sample Account"},"name": "Sample Schema","fields": [{"created_at": "2026-04-02T08:36:14.974937Z","data_type": {"data_type": "NUMBER","format": "INT64"},"designation": "FEATURE","id": "37fd2c48429440ae981cb9831c66464b","name": "age","created_by": {"id": "eca422469dc3270625bf646df3472a45","name": "Sample User"},"dimension": null,"semantic_type": "CONTINUOUS","unit": null,"updated_at": null,"vector": false},{"created_at": "2026-04-02T08:36:14.982037Z","data_type": {"data_type": "STRING","format": null},"designation": "FEATURE","id": "95dc4f2aab9242e491b1f109778b0d10","name": "occuptation","created_by": {"id": "eca422469dc3270625bf646df3472a45","name": "Sample User"},"dimension": null,"semantic_type": "CATEGORICAL","unit": null,"updated_at": null,"vector": false},{"created_at": "2026-04-02T08:36:15.063293Z","data_type": {"data_type": "NUMBER","format": "INT64"},"designation": "TARGET","id": "058383ab0f6c4ce9a078b5f842ace676","name": "income","created_by": {"id": "eca422469dc3270625bf646df3472a45","name": "Sample User"},"dimension": null,"semantic_type": "CATEGORICAL","unit": null,"updated_at": null,"vector": false}],"friendly_names": [],"category_friendly_names": [],"data_anchors": [],"data_protections": [],"created_at": "2026-04-02T08:36:14.968725Z","created_by": {"id": "eca422469dc3270625bf646df3472a45","name": "Sample User"}}
Save the returned id as SCHEMA_ID.
Create an ingestion configuration.
curl -X POST https://api.hi.umnai.com/data-processing-ingestion-configurations \-H "Authorization: Bearer <token>" \-H "Content-Type: application/json" \-d '{"name": "Sample Ingestion Configuration","data_schema_id": "c1342ae9bd985c0d9c5fa5222609c3fa"}'
Response
{"id": "d2f74730b1467213748b3750b3ad87ac","name": "Sample Ingestion Configuration","account": {"id": "e268443e43d93dab7ebef303bbe9642f","name": "Sample Account"},"created_at": "2026-03-15T10:15:30.000000Z","observation_specification": {"observation_type": "observation_type","observation_source": "observation_source","observation_shard_id": "observation_shard_id","observation_file_index": "observation_file_index","observation_index": "observation_index","observation_mask_index": "observation_mask_index","observation_mask_file_index": "observation_mask_file_index","observation_increment_index": "observation_increment_index"},"created_by": {"id": "dad46b2058752ffde5eaf02f1eb6abd5","name": "Sample User"},"updated_at": "2026-03-15T10:15:30.000000Z","data_schema": {"id": "c1342ae9bd985c0d9c5fa5222609c3fa","name": "Sample Data Schema"}}
Save the returned id as INGESTION_CONFIGURATION_ID.
4. Ingest the data
Run an initial ingestion job to create a datasource.
curl -X POST https://api.hi.umnai.com/data-processing-ingestion-jobs \-H "Authorization: Bearer <token>" \-H "Content-Type: application/json" \-d '{"job_type": "INITIAL","compute_configuration_id": "351df95006b1350e597762a1d58e9319","data_path": "/sample/initial/path","data_processing_configuration_id": "3dd973d3fda8604fba040381bfcaab29","datasource_name": "Sample Datasource","name": "Sample Initial Ingestion Job","storage_connector_id": "02910f90b30a5b94ad505efe83280dca"}'
Response
{"job_type": "INITIAL","account": {"id": "e268443e43d93dab7ebef303bbe9642f","name": "Sample Account"},"compute_configuration": {"id": "351df95006b1350e597762a1d58e9319","name": "Sample Compute Configuration"},"created_at": "2026-03-15T10:15:30.000000Z","data_path": "/sample/initial/path","data_processing_configuration": {"id": "3dd973d3fda8604fba040381bfcaab29","name": "Sample Ingestion Configuration"},"id": "c716d98913540a5968891bed979d177e","maximum_runtime_seconds": 3600,"name": "Sample Initial Ingestion Job","status": "STARTING","storage_connector": {"id": "02910f90b30a5b94ad505efe83280dca","name": "Sample Storage Connector"},"created_by": {"id": "dad46b2058752ffde5eaf02f1eb6abd5","name": "Sample User"},"datasource": {"id": "1ea7e575defdf6bc3f26a3f127e98170","name": "Sample Datasource"},"progress": 0,"stage": "initializing","stage_progress": 0,"updated_at": "2026-03-15T10:15:30.000000Z"}
Save the returned id as INGESTION_JOB_ID.
Retrieve the job until its status is COMPLETED.
Response
{"job_type": "INITIAL","account": {"id": "e268443e43d93dab7ebef303bbe9642f","name": "Sample Account"},"compute_configuration": {"id": "351df95006b1350e597762a1d58e9319","name": "Sample Compute Configuration"},"created_at": "2026-03-15T10:15:30.000000Z","data_path": "/sample/path","data_processing_configuration": {"id": "3dd973d3fda8604fba040381bfcaab29","name": "Sample Ingestion Configuration"},"id": "c716d98913540a5968891bed979d177e","maximum_runtime_seconds": 1,"name": "Sample Ingestion Job","status": "STARTING","storage_connector": {"id": "02910f90b30a5b94ad505efe83280dca","name": "Sample Storage Connector"},"created_by": {"id": "dad46b2058752ffde5eaf02f1eb6abd5","name": "Sample User"},"datasource": {"id": "1ea7e575defdf6bc3f26a3f127e98170","name": "Sample Datasource"},"progress": 0.5,"stage": "sample_stage","stage_progress": 0.5,"updated_at": "2026-03-15T10:15:30.000000Z"}
Save the returned datasource.id as DATASOURCE_ID.
5. Onboard the datasource
Create an initial onboarding configuration.
curl -X POST https://api.hi.umnai.com/data-processing-onboarding-configurations \-H "Authorization: Bearer <token>" \-H "Content-Type: application/json" \-d '{"configuration_type": "INITIAL","name": "Sample Initial Onboarding Configuration","prediction_type": "CLASSIFICATION","encoding_strategy": "STREAM","handle_unseen_categories": true,"impute": false,"one_hot_encoding_max_categories": 64,"strip_categories": true,"targets": ["target_column"]}'
Response
{"configuration_type": "INITIAL","account": {"id": "e268443e43d93dab7ebef303bbe9642f","name": "Sample Account"},"created_at": "2026-03-15T10:15:30.000000Z","encoding_strategy": "STREAM","feature_groups": [],"handle_unseen_categories": true,"id": "2df5b9fdb3389598e304f661315ebec8","impute": false,"infrequency_threshold": 1,"mask_category_fraction": 0.37,"mask_fraction": 0.8,"mask_median_fraction": 0.15,"mask_rows_fraction": 0.3,"name": "Sample Initial Onboarding Configuration","one_hot_encoding_max_categories": 64,"prediction_type": "CLASSIFICATION","shards": "256m","strip_categories": true,"sharding_strategy": "PROPORTIONATE","created_by": {"id": "dad46b2058752ffde5eaf02f1eb6abd5","name": "Sample User"},"targets": ["target_column"],"updated_at": "2026-03-15T10:15:30.000000Z"}
Save the returned id as ONBOARDING_CONFIGURATION_ID.
Run an initial onboarding job to create a dataset.
curl -X POST https://api.hi.umnai.com/data-processing-onboarding-jobs \-H "Authorization: Bearer <token>" \-H "Content-Type: application/json" \-d '{"job_type": "INITIAL","compute_configuration_id": "351df95006b1350e597762a1d58e9319","data_processing_configuration_id": "3dd973d3fda8604fba040381bfcaab29","dataset_name": "Sample Dataset","datasource_id": "1ea7e575defdf6bc3f26a3f127e98170","name": "Sample Initial Onboarding Job"}'
Response
{"job_type": "INITIAL","account": {"id": "e268443e43d93dab7ebef303bbe9642f","name": "Sample Account"},"compute_configuration": {"id": "351df95006b1350e597762a1d58e9319","name": "Sample Compute Configuration"},"created_at": "2026-03-15T10:15:30.000000Z","data_processing_configuration": {"id": "3dd973d3fda8604fba040381bfcaab29","name": "Sample Onboarding Configuration"},"datasource": {"id": "1ea7e575defdf6bc3f26a3f127e98170","name": "Sample Datasource"},"id": "7c2e73157a35658c245511bcf2a95195","maximum_runtime_seconds": 3600,"name": "Sample Initial Onboarding Job","status": "STARTING","created_by": {"id": "dad46b2058752ffde5eaf02f1eb6abd5","name": "Sample User"},"dataset": {"id": "3c4d09e4ef50b370ae0efacdb43ec2dd","name": "Sample Dataset"},"progress": 0,"stage": "initializing","stage_progress": 0,"updated_at": "2026-03-15T10:15:30.000000Z"}
Save the returned id as ONBOARDING_JOB_ID.
Retrieve the job until its status is COMPLETED.
Response
{"job_type": "INITIAL","account": {"id": "e268443e43d93dab7ebef303bbe9642f","name": "Sample Account"},"compute_configuration": {"id": "351df95006b1350e597762a1d58e9319","name": "Sample Compute Configuration"},"created_at": "2026-03-15T10:15:30.000000Z","data_processing_configuration": {"id": "3dd973d3fda8604fba040381bfcaab29","name": "Sample Onboarding Configuration"},"datasource": {"id": "1ea7e575defdf6bc3f26a3f127e98170","name": "Sample Datasource"},"id": "7c2e73157a35658c245511bcf2a95195","maximum_runtime_seconds": 1,"name": "Sample Onboarding Job","status": "STARTING","created_by": {"id": "dad46b2058752ffde5eaf02f1eb6abd5","name": "Sample User"},"dataset": {"id": "3c4d09e4ef50b370ae0efacdb43ec2dd","name": "Sample Dataset"},"progress": 0.5,"stage": "sample_stage","stage_progress": 0.5,"updated_at": "2026-03-15T10:15:30.000000Z"}
Save the returned dataset.id as DATASET_ID.
6. Train a model
Create a workspace for the training job and resulting model.
Response
{"id": "1629dee48cc4e53161f9b2be8614e062","account": {"id": "e268443e43d93dab7ebef303bbe9642f","name": "Sample Account"},"name": "Sample Workspace","created_at": "2026-03-24T13:12:54.215574Z","created_by": {"id": "dad46b2058752ffde5eaf02f1eb6abd5","name": "Sample User"}}
Save the returned id as WORKSPACE_ID.
Create the training compute configuration.
Response
{"id": "351df95006b1350e597762a1d58e9319","name": "Sample Compute Configuration","machine_type": "ml.m5.4xlarge","compute_type": "TRAINING","account": {"id": "e268443e43d93dab7ebef303bbe9642f","name": "Sample Account"},"created_at": "2026-03-25T15:23:50.965824Z","created_by": {"id": "dad46b2058752ffde5eaf02f1eb6abd5","name": "Sample User"}}
Save the returned id as TRAINING_COMPUTE_CONFIGURATION_ID.
Create a training configuration.
Response
{"model_training_type": "INDUCTION","account": {"id": "e268443e43d93dab7ebef303bbe9642f","name": "Sample Account"},"created_at": "2026-03-15T10:15:30.000000Z","id": "236f09a5d73018a9b8324105f1ee82df","name": "Sample Training Configuration","alpha": 0.5,"alpha_factor": 0.5,"bin_min_distance": 0.5,"binning_strategy": "UNIFORM","bins": 1,"boosting_strategy": "NO_GROUPING","created_by": {"id": "dad46b2058752ffde5eaf02f1eb6abd5","name": "Sample User"},"early_stopping_patience": 1,"estimators": 1,"estimators_winsorize_level": 0.5,"exclude_implicit": true,"init_depth": 1,"l1_ratio": 0.5,"l1_ratio_delta": 0.5,"learning_rate": 0.5,"learning_rate_decay": 0.5,"learning_rate_patience": 1,"log_interval": 1,"max_depth": 1,"max_depth_momentum": 1,"max_interaction_degree": 1,"max_interactions": 1,"max_iterations": 1,"max_polynomial_degree": 1,"max_rounds": 1,"max_samples": 0.5,"model_capabilities": {"has_causal": false,"estimators_data": false},"model_complexity_max_iterations": 1,"model_complexity_patience": 1,"partition_min_delta": 0.5,"partition_min_delta_factor": 0.5,"partition_min_delta_fraction": 0.5,"partition_min_samples": 1,"prune_patience": 1,"prune_threshold": 0.5,"sparse_model": true,"subsample": 0.5,"training_size": 0.5,"updated_at": "2026-03-15T10:15:30.000000Z","validation_size": 0.5}
Save the returned id as TRAINING_CONFIGURATION_ID.
Run the training job.
Use WORKSPACE_ID as the X-Active-Workspace header value.
curl -X POST https://api.hi.umnai.com/training-jobs \-H "X-Active-Workspace: X-Active-Workspace" \-H "Authorization: Bearer <token>" \-H "Content-Type: application/json" \-d '{"name": "Sample Training Job","dataset_id": "3c4d09e4ef50b370ae0efacdb43ec2dd","training_configuration_id": "236f09a5d73018a9b8324105f1ee82df","compute_configuration_id": "351df95006b1350e597762a1d58e9319","model_name": "Sample Model Name","managed_spot_training": false}'
Response
{"id": "5647cb5d7800f6834be0f591838d5d9a","name": "Sample Training Job","dataset": {"id": "3c4d09e4ef50b370ae0efacdb43ec2dd","name": "Sample Dataset"},"account": {"id": "e268443e43d93dab7ebef303bbe9642f","name": "Sample Account"},"workspace": {"id": "1629dee48cc4e53161f9b2be8614e062","name": "Sample Workspace"},"training_configuration": {"id": "236f09a5d73018a9b8324105f1ee82df","name": "Sample Training Configuration"},"compute_configuration": {"id": "351df95006b1350e597762a1d58e9319","name": "Sample Compute Configuration"},"status": "STARTING","managed_spot_training": false,"maximum_runtime_seconds": 3600,"created_at": "2026-03-15T10:15:30.000000Z","model": {"id": "20f35e630daf44dbfa4c3f68f5399d8c","name": "Sample Model"},"maximum_wait_time_seconds": 3600,"model_constraints": [],"model_constraint_groups": [],"created_by": {"id": "dad46b2058752ffde5eaf02f1eb6abd5","name": "Sample User"},"updated_at": "2026-03-15T10:15:30.000000Z","stage": "sample_stage","progress": 0.5,"stage_progress": 0.5}
Save the returned id as TRAINING_JOB_ID.
Retrieve the job until its status is COMPLETED.
Response
{"id": "5647cb5d7800f6834be0f591838d5d9a","name": "Sample Training Job","dataset": {"id": "3c4d09e4ef50b370ae0efacdb43ec2dd","name": "Sample Dataset"},"account": {"id": "e268443e43d93dab7ebef303bbe9642f","name": "Sample Account"},"workspace": {"id": "1629dee48cc4e53161f9b2be8614e062","name": "Sample Workspace"},"training_configuration": {"id": "236f09a5d73018a9b8324105f1ee82df","name": "Sample Training Configuration"},"compute_configuration": {"id": "351df95006b1350e597762a1d58e9319","name": "Sample Compute Configuration"},"status": "STARTING","managed_spot_training": false,"maximum_runtime_seconds": 3600,"created_at": "2026-03-15T10:15:30.000000Z","model": {"id": "20f35e630daf44dbfa4c3f68f5399d8c","name": "Sample Model"},"maximum_wait_time_seconds": 3600,"model_constraints": [],"model_constraint_groups": [],"created_by": {"id": "dad46b2058752ffde5eaf02f1eb6abd5","name": "Sample User"},"updated_at": "2026-03-15T10:15:30.000000Z","stage": "sample_stage","progress": 0.5,"stage_progress": 0.5}
Save the returned model.id as MODEL_ID.
7. Deploy the model
Create a serverless deployment configuration.
curl -X POST https://api.hi.umnai.com/deployment-configurations \-H "Authorization: Bearer <token>" \-H "Content-Type: application/json" \-d '{"deployment_type": "SERVERLESS","maximum_concurrency": 1,"memory_size": 1024,"name": "Sample Serverless Deployment Configuration"}'
Response
{"deployment_type": "SERVERLESS","account": {"id": "e268443e43d93dab7ebef303bbe9642f","name": "Sample Account"},"created_at": "2026-03-15T10:15:30.000000Z","id": "8be6a0ea7db3303d3fb3568c0b583d9d","maximum_concurrency": 1,"memory_size": 1024,"name": "Sample Serverless Deployment Configuration","created_by": {"id": "dad46b2058752ffde5eaf02f1eb6abd5","name": "Sample User"},"updated_at": "2026-03-15T10:15:30.000000Z"}
Save the returned id as DEPLOYMENT_CONFIGURATION_ID.
Deploy the model.
curl -X POST https://api.hi.umnai.com/deployed-models \-H "Authorization: Bearer <token>" \-H "Content-Type: application/json" \-d '{"name": "Sample Deployed Model","model_id": "20f35e630daf44dbfa4c3f68f5399d8c","deployment_configuration_id": "8be6a0ea7db3303d3fb3568c0b583d9d"}'
Response
{"id": "abf66c365bddf8b064bdfe2ca5ee662a","account": {"id": "e268443e43d93dab7ebef303bbe9642f","name": "Sample Account"},"name": "Sample Deployed Model","status": "STARTING","created_at": "2026-03-15T10:15:30.000000Z","model": {"id": "20f35e630daf44dbfa4c3f68f5399d8c","name": "Sample Model"},"deployment_configuration": {"id": "8be6a0ea7db3303d3fb3568c0b583d9d","name": "Sample Deployment Configuration"},"created_by": {"id": "dad46b2058752ffde5eaf02f1eb6abd5","name": "Sample User"},"updated_at": "2026-03-15T10:15:30.000000Z","tags": [{"name": "Sample Tag"}]}
Save the returned id as DEPLOYED_MODEL_ID.
Retrieve the deployed model until its status is RUNNING.
Response
{"id": "abf66c365bddf8b064bdfe2ca5ee662a","account": {"id": "e268443e43d93dab7ebef303bbe9642f","name": "Sample Account"},"name": "Sample Deployed Model","status": "STARTING","created_at": "2026-03-15T10:15:30.000000Z","model": {"id": "20f35e630daf44dbfa4c3f68f5399d8c","name": "Sample Model"},"deployment_configuration": {"id": "8be6a0ea7db3303d3fb3568c0b583d9d","name": "Sample Deployment Configuration"},"created_by": {"id": "dad46b2058752ffde5eaf02f1eb6abd5","name": "Sample User"},"updated_at": "2026-03-15T10:15:30.000000Z","tags": [{"name": "Sample Tag"}]}
8. Request a prediction and explanation
Query the deployed model with a results view request.
curl -X POST https://api.hi.umnai.com/deployed-models/deployedModelId/views \-H "X-Views-Cache-Control: no-cache, no-store" \-H "Authorization: Bearer <token>" \-H "Content-Type: application/json" \-d '{"data": [{"view_type": "RESULTS"}],"query": {"age": [58],"capital_45_gain": [0],"capital_45_loss": [0],"education": ["HS-grad"],"educational_45_num": [9],"fnlwgt": [299831],"gender": ["Male"],"hours_45_per_45_week": [35],"marital_45_status": ["Married-civ-spouse"],"native_45_country": ["United-States"],"occupation": ["?"],"race": ["White"],"relationship": ["Husband"],"workclass": ["?"]},"targets": {"income": [0]}}'
Response
{"data": [{"view_type": "RESULTS","commons_version": {"major_version": 0,"minor_version": 18,"patch_version": 0,"build_version": "dev72"},"core_version": {"major_version": 7,"minor_version": 10,"patch_version": 0,"build_version": "dev52"},"output_version": {"major_version": 2,"minor_version": 0,"patch_version": 0},"view_data": {"model_info": {"columns": ["model_intercept","model_id","dataset_name","commons_version","core_version","estimators_aggregation_type","estimators_aggregation_weights","estimators_aggregation_bias"],"index": [0],"data": [[-1.0836982274,"2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533","2bcb9dc8-adult_income-root","0.18.0.dev72","7.10.0.dev52","MEAN",null,[0]]],"foreign_keys": [],"labels": [],"types": [{"data_type": "NUMBER","format": "FLOAT64"},{"data_type": "STRING","format": null},{"data_type": "STRING","format": null},{"data_type": "STRING","format": null},{"data_type": "STRING","format": null},{"data_type": "STRING","format": null},{"data_type": 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A successful response includes the prediction result and the explanation data associated with that result.
Complete
You have ingested sample data, onboarded it into a dataset, trained a model, deployed it, and requested a prediction with explanation data.
Use the guides for deeper implementation details, or use the API reference for endpoint and schema details.