Welcome to the Pyiris documentation

Documentation accord to the last Pyiris version.

This library provides tools for building a more user-friendly interface with all of our Data Products. It abstracts the usage and standardize the way we Read, Transform and Write data within our whole platform. It also helps users exploring, transforming, training and serving machine learning models. We will be able to monitor, optimize and enhance the overall user experience if we have the same starting point and also make our PR’s be approved fast, since a lot of validations will happen under the hood, before going to an SLA-based PR Review later on.

If anything in the back-end needs to be changed in the future, little to no refactoring will be necessary in the business teams, and the Data pipeline and Machine Learning repositories will remain standardized and barely untouched. It will also make it easier for someone to understand exactly what is going on within the transformations, since we have a more clean and user-friendly syntax.

Although it was conceived and built by the Iris platform team, it is meant to be a product for the entire Analytics team at Ambev, so feel free to reach out and even contribute to and extend our library if you want to.

By the way, to be more friendly, we separate the code repository and the documentation based on an ETL process, so our core documentation is based on this. However, here you can find docs about the intelligence sub-model, setup configuration and usage examples too.

Pyiris documentation