inferyx 1.0.6

A python package for Inferyx analytics tool

inferyx

A Python SDK for the Inferyx AI/Data Analytics platform.

inferyx lets you programmatically create, manage, and orchestrate Inferyx platform entities — datasources, data pipelines, data products, business rules, ML models, schedulers, and more — directly from Python.

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Installation

pip install inferyx

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Getting started

Every operation starts with an AppConfig, which holds your platform host and authentication tokens.

from inferyx.config import AppConfig
from inferyx.components.admin import Datasource

app_config = AppConfig(
    host="analytic.inferyx.com",
    appToken="<YOUR_APP_TOKEN>",
    adminToken="<YOUR_ADMIN_TOKEN>",
)

datasource = Datasource(
    app_config=app_config,
    name="my_postgres_source",
    type="POSTGRES",
)

response = datasource.create()
print(response)

You can also authenticate using a JWT token (with optional auto-refresh):

app_config = AppConfig(
    host="analytic.inferyx.com",
    jwt_token="<JWT_TOKEN>",
    token_refresher=lambda: get_new_jwt_token(),
)

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What you can do

The SDK is organized into components, each mapping to a core area of the Inferyx platform:

Component	Module	Description
Admin	inferyx.components.admin	Manage datasources and resources
Batch Scheduler	inferyx.components.batch_scheduler	Create and run batches, manage schedulers
Business Rule	inferyx.components.business_rule	Define and manage business rules and criteria
Data Asset	inferyx.components.data_asset	Manage data assets
Data Catalog	inferyx.components.data_catalog	Manage glossaries, domains, assets, and products in the catalog
Data Classification	inferyx.components.data_classification	Classify data
Data Domain	inferyx.components.data_domain	Manage data domains
Data Glossary	inferyx.components.data_glossary	Manage business glossary terms
Data Ingestion	inferyx.components.data_ingestion	Configure ingestion jobs, filters, and transformations
Data Operator	inferyx.components.data_operator	Build and configure custom data operators
Data Pipeline	inferyx.components.data_pipeline	Build pipelines with tasks and stages
Data Preparation	inferyx.components.data_preparation	Prepare datasets and datapods with transformations and mappings
Data Product	inferyx.components.data_product	Manage data products
Data Profiling	inferyx.components.data_profiling	Profile datasets
Data Quality	inferyx.components.data_quality	Define and run data quality checks
Data Reconciliation	inferyx.components.data_reconciliation	Reconcile data across sources
Data Science	inferyx.components.data_science	Manage models, training, prediction, evaluation, and feature stores
File Manager	inferyx.components.file_manager	Upload, download, and manage files
Job Monitoring	inferyx.components.job_monitoring	Monitor job and execution status
Vizpod	inferyx.components.vizpod_component	Manage visualization pods
Workbench	inferyx.components.workbench	Interact with the Inferyx workbench

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Example: Deploying a model

from inferyx.config import AppConfig
from inferyx.components.data_science import ModelDeploy

app_config = AppConfig(
    host="analytic.inferyx.com",
    appToken="<YOUR_APP_TOKEN>",
    adminToken="<YOUR_ADMIN_TOKEN>",
)

deploy_instance = ModelDeploy(app_config)

response = deploy_instance.deploy(
    file="/path/to/model.pmml",
    resource_name="resource_local_deploy",
    datasource_name="file_upload_s3_source",
    model_name="my_model",
    model_type="PMML",
)

print(response.text)

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Requirements

• Python 3.8+
• A valid Inferyx platform host and access tokens

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Links

• Homepage
• Bug Tracker

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License

Copyright © Inferyx. All rights reserved.