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scikit-learn {: .label .label-yellow } chemometrics {: .label .label-yellow } spectroscopy {: .label .label-yellow } machine learning {: .label .label-yellow } data science {: .label .label-yellow }
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This project provides a toolkit for spectral preprocessing techniques integrated with the widely popular scikit-learn API and the rest of the Python machine learning environment. The goal of the project is to provide a comprehensive and user-friendly package to standardize the development and deployment of chemometric models. The project intends to enable users to easily build and deploy machine learning and chemometric models on top of preprocessed data, making it possible to identify patterns and make predictions with greater accuracy.
chemotools provides a tool-kit to integrate chemometric techniques with the rest of the Python machine learning environment. Explore your spectral data, build and train chemometric models, and understand the results. Use chemotools to preprocess your data and scikit-learn to build and train your models.
Easily and efficiently reuse your trained machine learning models across different projects without the need for time-consuming retraining. chemotools offers seamless persistence of models using common formats such as pickle or joblib, allowing you to easily reload and deploy them in a variety of settings.
After training a machine learning model using scikit-learn and chemotools, the next step is to deploy it to a production environment. Create an API using a your favorite framework. The API can receive input data and return predictions from the trained model. Once the API is built, it can be containerized using Docker, which provides an isolated environment to run the API. Creating a Docker image enables easy distribution and deployment of the model to any environment that supports Docker. By deploying the models through an API and Docker, organizations can scale the model's usage, monitor its performance, and quickly update or roll back the model when needed.