featrixclient


Namefeatrixclient JSON
Version 2024.916.1 PyPI version JSON
download
home_pagehttps://featrix.com
SummaryFeatrix AI API
upload_time2024-09-16 18:43:17
maintainerNone
docs_urlNone
authorFeatrix, Inc.
requires_python>=3.10
licenseMIT License Copyright (c) 2024 Featrix, Inc Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
keywords ml ai embedding embeddings tabular embeddings vectorize automl xgboost neuralnetwork eda
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls. 
            Welcome to Featrix!

Featrix is a data gateway to create ML models for structured data with no data preparation on your part. Featrix comes with a Python client library for ML engineers and data scientists to work with any structured data source, including Pandas dataframes. Featrix is powered by a hosted SaaS or private Docker containers deployed on site in private clouds with an enterprise license.

Getting started is easy and involves just a few steps:

Load your baseline training data into a Featrix “data space”.
Train a “vector space” on that data. This transforms the original data into vectors that you can leverage for models or querying.
At this point, you can cluster the vectors or query for nearest neighbors with no further work.
You can also train a downstream prediction model for a target column. The target column can be in the original data, or it can be something specific to the model itself.
Then you can run the model. The model can be presented with partial records and it returns values for the target.
A few notes on why we have picked these abstractions:

The data space lets you mix and match source data into different configurations or arrangements without having to reload the data.
Manually joining data is not required to associate data in the data space; Featrix infers likely combinations to associate data and you can choose to override these if needed.
A data space can have multiple vector spaces with different arrangements.
A vector space can have multiple models.
Every vector space includes a set of vector indices to enable extremely fast querying for clusters or nearest neighbors in the data set.

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Featrix, Inc.
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