This repo features an Azure Machine Learning (AML) Acceleration template which enables you to quickly onboard your existing Machine Learning code to AML. The template enables a smooth ML development process between your local machine and the Azure Cloud. Furthermore, it includes simple examples for running your model's training and batch inferecing as Machine Learning Pipelines for automation.
If you want to follow a guided approach to use this repo, start with migrating your first workload to AML and walk through the individual sections.
We recommend you to start with migrating your first workload to AML as it covers all prerequisites and outlines a simple and proven step-by-step approach.
This repo follows a pre-defined structure for storing your model code, pipelines, etc.
| File/folder | Description |
|---|---|
automation |
Azure DevOps based CI/CD pipelines for MLOps |
instructions\ |
A step-by-step guide on how to onboard your first workload to AML |
sample-data\ |
Some small sample data used for the template example |
src\ |
Model(s) code and other required code assets |
src\model1 |
A full end-to-end example for training, real-time and batch inferencing and automation |
pipelines-yaml\ |
A set of YAML-based ML pipelines |
pipelines-py\ |
A set of Python-based ML pipelines |
- Clemens Siebler, AI Technical Specialist GBB EMEA
- Erik Zwiefel, AI Principal Technical Specialist GBB Americas
- Alan Weaver, AI Senior Technical Specialist GBB EMEA
- Alexander Zeltov, AI Principal Technical Specialist GBB Americas
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