Beyond the Basics with Azure ML: Conclusion

This is part seven in a series on getting beyond the basics with Azure ML. What Did We Learn? Throughout this series, we gained a better understanding of how we can use Azure Machine Learning for more than drag-and-drop model training. In the first post of the series, we looked at using notebooks in Azure…

Beyond the Basics with Azure ML: MLOps

This is part six in a series on getting beyond the basics with Azure ML. A Better Way for Deploying Code One development we've seen in software engineering over the past couple of decades has been the automation of code deployment leading to more frequent deployment of smaller sets of code changes. This in turn…

Beyond the Basics with Azure ML: ML Pipelines

This is part five in a series on getting beyond the basics with Azure ML. Pipelines Everywhere Azure ML is built around the notion of pipelines. With machine learning pipelines, we perform the process of data cleansing, data transformation, model training, model scoring, and model evaluation as different steps in the pipeline. Then, after we…

Beyond the Basics with Azure ML: Model Tracking

This is part four in a series on getting beyond the basics with Azure ML. A Prerequisite: MLflow Before we dive into this post, I ask you to read a prior post I wrote about MLflow. That post lays out four key products in MLflow and how they all work together to make model management…

Beyond the Basics with Azure ML: The Python SDK

This is part three in a series on getting beyond the basics with Azure ML. The Python SDK Over the past two posts, we have started using the Azure Machine Learning SDK for Python but I've only touched on the topic. In this post, we are going to dive into the topic. Installing the Python…

Beyond the Basics with Azure ML: Working with VS Code

This is part two in a series on getting beyond the basics with Azure ML. Making Things Personal Notebooks are great for ad hoc work or simple data analysis but we will want more robust tools if we wish to perform proper code development, testing, and deployment. This is where Visual Studio Code comes into…

Beyond the Basics with Azure ML: Working with Notebooks

This is part one in a series on getting beyond the basics with Azure ML. In the prior series, Low-Code Machine Learning with Azure ML, we saw how to get started with Azure Machine Learning in a fairly pain-free way, especially for developers getting started with machine learning. In this series, I will assume that…

Low-Code Azure ML: Wrapping Up

This is part seven in a series on low-code machine learning with Azure ML. What Did We Learn? Over the course of this series, we learned how to use Azure Machine Learning to train models and score data without writing a line of code. We first learned how to create a workspace in Azure Machine…

Low-Code ML: Batch Scoring

This is part six in a series on low-code machine learning with Azure ML. Last Time on 36 Chambers In the prior episode of this series, we built a real-time endpoint, showed how to call it, and saw how to make some minor changes to a real-time inference pipeline. Today, we're going to show off…

Low-Code ML: Deployment and Endpoints

This is part five in a series on low-code machine learning with Azure ML. Where We Are In the prior post, we trained a model using the Azure ML designer. Penguins scored In this post, we're going to make it available for the whole world to use, as what the world needs is a way…