ParallelModel documentation
Build applications using ParallelModel's machine learning APIs.
Integrate machine learning workflows into your applications with APIs for managing models and datasets, submitting processing jobs, monitoring execution, and retrieving results.
Explore step-by-step guides and detailed API references to get started.
Authentication
Authenticate your application and authorize access to ParallelModel APIs using access tokens and scopes.
Authentication API Reference
Get an access token using your application's client credentials, then include it as a bearer token in API requests.
Blob Storage
Manage datasets, model artifacts, and processing outputs using ParallelModel's blob storage APIs.
Blob Management
Explore the API documentation for each blob operation.
Machine Learning Pipeline
Explore API references for building and running machine learning workflows with ParallelModel.
Machine Learning
Browse the ParallelModel module documentation.
- parallelmodel
- parallelmodel.base
- parallelmodel.calibration
- parallelmodel.callback
- parallelmodel.cluster
- parallelmodel.compose
- parallelmodel.covariance
- parallelmodel.cross_decomposition
- parallelmodel.datasets
- parallelmodel.decomposition
- parallelmodel.discriminant_analysis
- parallelmodel.dummy
- parallelmodel.ensemble
- parallelmodel.exceptions
- parallelmodel.experimental
- parallelmodel.feature_extraction
- parallelmodel.feature_selection
- parallelmodel.frozen
- parallelmodel.gaussian_process
- parallelmodel.impute
- parallelmodel.inspection
- parallelmodel.isotonic
- parallelmodel.kernel_approximation
- parallelmodel.kernel_ridge
- parallelmodel.linear_model
- parallelmodel.manifold
- parallelmodel.metrics
- parallelmodel.mixture
- parallelmodel.model_selection
- parallelmodel.multiclass
- parallelmodel.multioutput
- parallelmodel.naive_bayes
- parallelmodel.neighbors
- parallelmodel.neural_network
- parallelmodel.pipeline
- parallelmodel.preprocessing
- parallelmodel.random_projection
- parallelmodel.semi_supervised
- parallelmodel.svm
- parallelmodel.tree
- parallelmodel.utils
Artificial Intelligence Pipeline
Explore tools and APIs for building artificial intelligence workflows.
Coming soon
Guides and API references for artificial intelligence pipelines will be available here.
Third-Party Licences
ParallelModel uses open-source software. We acknowledge the developers and contributors behind the following packages.
| Package | Version | Licence summary |
|---|---|---|
| aiohappyeyeballs | 2.7.1 | PSF-2.0 |
| aiohttp | 3.14.3 | Apache-2.0 AND MIT |
| aiosignal | 1.4.0 | Apache-2.0 |
| attrs | 26.1.0 | MIT |
| azure-core | 1.41.0 | MIT |
| azure-identity | 1.25.3 | MIT |
| azure-servicebus | 7.14.3 | MIT |
| certifi | 2026.7.22 | MPL-2.0 |
| cffi | 2.1.1 | MIT-0 (MIT No Attribution) |
| charset-normalizer | 3.5.1 | MIT |
| cloudpickle | 3.1.2 | BSD-3-Clause |
| cryptography | 50.0.1 | Apache-2.0 OR BSD-3-Clause; bundled components have additional notices |
| frozenlist | 1.8.0 | Apache-2.0 |
| idna | 3.20 | BSD-3-Clause |
| isodate | 0.7.2 | BSD-3-Clause |
| joblib | 1.6.0 | BSD-3-Clause |
| msal | 1.39.0 | MIT |
| msal-extensions | 1.3.1 | MIT |
| multidict | 6.9.1 | Apache-2.0 |
| narwhals | 2.27.1 | MIT |
| NumPy | 2.5.4 | BSD-3-Clause AND 0BSD AND MIT AND Zlib AND CC0-1.0; additional binary-component notices |
| pandas | 3.0.6 | BSD-3-Clause; bundled BSD, MIT, Apache and Python licence notices |
| propcache | 0.5.4 | Apache-2.0 |
| pycparser | 3.0 | BSD-3-Clause |
| PyJWT | 2.15.1 | MIT |
| python-dateutil | 2.9.0.post0 | BSD-3-Clause; Apache-2.0 also available for specified contributions |
| requests | 2.34.2 | Apache-2.0 |
| scikit-learn | 1.9.1 | BSD-3-Clause; bundled array API components use MIT |
| SciPy | 1.18.1 | BSD-3-Clause; additional bundled-component licences and notices |
| six | 1.17.0 | MIT |
| threadpoolctl | 3.7.0 | BSD-3-Clause |
| typing_extensions | 4.16.0 | PSF-2.0 |
| tzdata | 2026.5 | Apache-2.0 for the Python package; IANA time-zone data is public domain |
| urllib3 | 2.8.0 | MIT |
| yarl | 1.25.1 | Apache-2.0 |
Bundled Components
Binary distributions may include additional components with their own licences. The inspected Linux NumPy and SciPy wheels include notices for OpenBLAS, LAPACK, the GCC runtime and libquadmath. Other builds may contain different components.
- OpenBLAS: BSD-3-Clause.
- LAPACK: BSD-3-Clause-Open-MPI.
- GCC runtime: GPL-3.0-or-later WITH GCC-exception-3.1.
- libquadmath: LGPL-2.1-or-later.
This table summarises third-party licences. The original copyright notices, licence texts, exceptions and notices supplied with each package and bundled component remain applicable. References to these projects do not imply endorsement of ParallelModel by their developers or contributors.