Advent of 2022, Day 15 – Getting to know MLflow

This article is originally published at https://tomaztsql.wordpress.com

In the series of Azure Machine Learning posts:

• Dec 01: What is Azure Machine Learning?
• Dec 02: Creating Azure Machine Learning Workspace
• Dec 03: Understanding Azure Machine Learning Studio
• Dec 04: Getting data to Azure Machine Learning workspace
• Dec 05: Creating compute and cluster instances in Azure Machine Learning
• Dec 06: Environments in Azure Machine Learning
• Dec 07: Introduction to Azure CLI and Python SDK
• Dec 08: Python SDK namespaces for workspace, experiments and models
• Dec 09: Python SDK namespaces for environment, and pipelines
• Dec 10: Connecting to client using Python SDK namespaces
• Dec 11: Creating Pipelines with Python SDK
• Dec 12: Creating jobs
• Dec 13: Automated ML
• Dec 14: Registering the models

MLFlow is an open-source framework for registering, managing and tracking machine learning models. It is multiplatform, bringing consistent model training and model consumption across different platforms. This means, that training a model locally and uploading it to Azure or training a model on remote compute instances and downloading it, is a great feature for MLflow.

You can use MLflow with Azure CLI, Azure Python SDK or in the studio and it will deliver a consistent experience (note, some functionalities are limited to the language).

It offers model registering and logging, and model deployment.

By opening a new notebook and attaching it to the compute, you can start setting up the MLflow.

And we can also view the metrics and also artefacts.

We use couple of important methods within the mlflow namespace:

• MLClient – to setup the credentials and getting the job runs and viewing metrics
• mlflow – to create and set the tracking URI and set the experiment

Tomorrow we will be using xgboost on a sample dataset and use MLflow for tracking and logging.

Compete set of code, documents, notebooks, and all of the materials will be available at the Github repository: https://github.com/tomaztk/Azure-Machine-Learning

Happy Advent of 2022! 

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