{"categories":[{"label":"Utilities","url":"https://skillfed.io/packages/category/utilities/8"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"},{"label":"Database","url":"https://skillfed.io/packages/category/database/4"}],"enrichment":{"capability":"Teradata ModelOps Client provides a CLI and SDK for managing machine learning model lifecycle\u2014training, evaluation, deployment, and versioning\u2014within Teradata's data platform.","skillfed_tags":["teradata-vantage","model-lifecycle","feature-engineering"],"use_cases":["Train and validate ML models locally before committing to a shared ModelOps project repository.","Automate feature engineering workflows and compute feature statistics across datasets.","Deploy trained model versions to Teradata Vantage for production inference.","Clone and link local development repositories to remote ModelOps projects for team collaboration.","Diagnose and troubleshoot ModelOps configuration and authentication issues via the doctor command."],"what_it_does":"Teradata ModelOps Client is a command-line tool and Python SDK for managing the full lifecycle of machine learning models on Teradata's Vantage platform. It bridges local development and remote model management by allowing data scientists to initialize projects, train and evaluate models locally before committing to version control, manage feature engineering tasks, and deploy model versions to production. The package integrates with git for repository management and provides OAuth-based authentication to Teradata ModelOps instances.\n\nThe CLI supports interactive and non-interactive workflows for common tasks: listing projects, models, and datasets; cloning project repositories; running model training and evaluation; managing connections and feature statistics; and diagnosing configuration issues. The SDK layer exposes the same capabilities programmatically, enabling automation and integration into larger data pipelines. It depends on standard libraries (requests, pyyaml, gitpython, cryptography) plus Teradata's own teradataml for database connectivity.","worth_installing":"Yes, if you are a Teradata Vantage user developing ML models within that ecosystem. The package is actively maintained, has no known vulnerabilities, and low install friction. However, the proprietary license restricts use to internal purposes tied to a Teradata database license, and Windows users must manually install OpenSSL. Not suitable for open-source or non-Teradata workflows."},"id":"teradatamodelops","links":{"html":"https://skillfed.io/packages/teradatamodelops","md":"https://skillfed.io/packages/teradatamodelops.md","pypi":"https://pypi.org/project/teradatamodelops/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-19","license_spdx":null,"license_treatment":"unclear","name":"teradatamodelops","python_support":"supports_current","summary":"Python client for Teradata ModelOps (TMO)"},"popularity":{"monthly_downloads":154448,"position":10852,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"7.3.4"}