teradatamodelops
Python client for Teradata ModelOps (TMO)
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- On Windows, OpenSSL must be installed manually to authenticate with ModelOps instance.
- Requires Teradata Relational Database license and valid ModelOps credentials in ~/.tmo/config.yaml.
- Low install friction; pure Python wheel with no compiled dependencies.
License · maintenance · safety
(unclear) — Proprietary license with export control restrictions. Use is limited to internal purposes facilitating Teradata Relational Database licensing. Redistribution, modification, and reverse engineering are prohibited without written consent.
last release 2026-06-19 (56 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 154,448 downloads/mo, #10,852 on PyPI
Alternatives
Verify before relying
pip install teradatamodelops
from teradatamodelops import Client
client = Client()
projects = client.list_projects()- Whether SDK methods beyond list_projects are documented in the fact sheet or require external documentation review.
- Specific authentication flow and credential management details beyond config.yaml location.
- Performance characteristics and scalability limits for large model repositories or datasets.
What it is and 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.
The 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
teradatamodelops on PyPI
Before you install
Low install friction; pure Python wheel with no compiled dependencies. Actively maintained as of 56 days ago. Requires Python 3.10 or later.
On Windows, OpenSSL must be installed manually to authenticate with ModelOps instance. Requires Teradata Relational Database license and valid ModelOps credentials in ~/.tmo/config.yaml.
License in practice
Proprietary license with export control restrictions. Use is limited to internal purposes facilitating Teradata Relational Database licensing. Redistribution, modification, and reverse engineering are prohibited without written consent.
Quickstart
pip install teradatamodelops
from teradatamodelops import Client
client = Client()
projects = client.list_projects()Verify before relying
- Whether SDK methods beyond list_projects are documented in the fact sheet or require external documentation review.
- Specific authentication flow and credential management details beyond config.yaml location.
- Performance characteristics and scalability limits for large model repositories or datasets.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 13 packagesjinja2requestsrequests-oauthliboauthlibaiapyyamlgitpythoncryptographyteradatamlcertifimatplotlibnumpypandas |
| Maintenance | Actively maintained 56 days since the last release |
| First released | |
| Downloads | 154,448 / month, #10,852 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Financial and Insurance IndustryIntended Audience :: Healthcare IndustryIntended Audience :: Information TechnologyIntended Audience :: ManufacturingIntended Audience :: Other AudienceIntended Audience :: Science/ResearchLicense :: Other/Proprietary LicenseProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: DatabaseTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Utilities |
Evidence: teradatamodelops-7.3.4-py3-none-any.whl
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See also teradataml · teradata · apache-airflow-providers-teradata · mlflow · matrice · teradatasqlalchemy · sagemaker-serve · azureml-pipeline · sagemaker-mlops · teradatasql