This tutorial will use a well-known MNIST dataset to train the model to recognize handwritten digits. And, we’ll do (almost) everything without leaving Visual Studio Code to make things even more streamlined. We’ll begin by training and testing a model using the XGBoost library with Azure Machine Learning services. You can find the sample code for this article on GitHub. In this three-part series, we’ll work through several approaches to creating and using machine learning models in Python on Azure. Azure is a great place to run Python-based ML workloads of almost any size. However, machine learning often requires significant processing power, which may exceed the capabilities of whatever computer you have at a given moment. You can run Python code on almost everything: PC, Mac, or Raspberry Pi, with x86, x86-64, AMD64, or ARM/ARM64 processors, and Windows, Linux, or macOS operating systems. You most likely think of Python when you think about machine learning code.
It took first place in the TIOBE Index in November, and it was the third most popular programming language in Stack Overflow's 2021 Developer Survey. As of 2021, the Python language is officially more popular than well-established old-timers like Java, C#, or C++. This trend was one of the biggest drivers of Python’s explosion in popularity. It’s hard to miss the increasing popularity and prevalence of machine learning (ML) and data science.
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