MLOps Recipes
A library of minimalistic & modular recipes. Easily compose your custom end to end CI/CD pipelines for Machine Learning
Check out the Project website for more details
Status: Project board
Technologies: Azure Machine Learning & Azure Devops
Get Started
Note: Automated builds based on code/asset changes have been disabled by setting triggers: none
in the pipelines. The reason is to avoid triggering accidental builds during your learning phase.
Notes on our Base scenario:
- Directory Structure
mlops
contains the devops pipelinesmodel_pipelines
contains individual pipelines for each of the models. These leverage the recipesrecipes
: contains parameterized, reusable devops pipeline for different scenarios
models
directory has the source code for the individual models (training, scoring etc)setup
directory contains documentation on usage
- Training: For training we use a simple LogisticRegression model on the German Credit card dataset. We build sklearn pipeline that does feature engineering. We export the whole pipeline as a the model binary (pkl file).
- We use Azure ML CLI as a mechanism for interacting with Azure ML due to simplicity reasons.
Acknowledgements
- MLOpsPython python repo was one of the inspirations for this - thanks to the contributors
- German Creditcard Dataset
Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.