Introduction

What Azure ML Pipelines?

An Azure Machine Learning pipeline is an independently executable workflow of a complete machine learning task. An Azure Machine Learning pipeline helps to standardize the best practices of producing a machine learning model, enables the team to execute at scale, and improves the model building efficiency.

Why to integrate Kedro project with Azure ML Pipelines?

Throughout couple years of exploring ML Ops ecosystem as software developers we’ve been looking for a framework that enforces the best standards and practices regarding ML model development and Kedro Framework seems like a good fit for this position, but what happens next, once you’ve got the code ready?

It seems like the ecosystem grown up enough so you no longer need to release models you’ve trained with Jupyter notebook on your local machine on Sunday evening. In fact there are many tools now you can use to have an elegant model delivery pipeline that is automated, reliable and in some cases can give you a resource boost that’s often crucial when handling complex models or a load of training data. With the help of some plugins You can develop your ML training code with Kedro and execute it using multiple robust services without changing the business logic.

We currently support:

And with this kedro-azureml plugin, you can run your code on Azure ML Pipelines in a fully managed fashion

../_images/azureml_running_pipeline.gifAzure ML Pipelines