Azure Automated ML offers a quick and easy way to train baseline models for all sorts of machine learning tasks such as regression, classification, and time series forecasting. In this article I’ll show you how to reverse engineer an Azure AutoML model, decompose it into its atomic components, and use those components to create your own model, all without any Azure ML SDK dependencies.
But why train your own model instead of relying on automated machine learning? For starters, AutoML is good at training one-off models but most models need regular retraining to maintain their performance. For example, I’ve previously used AutoML to train a time series forecasting model, and used that model to predict tomorrow’s closing price for Ethereum. Training a one-off automated ML model worked acceptably well, however its forecasts got worse and worse the farther we went into the future1. A one-off model is definitely not a good solution in this case.
Read more on Vlad’s blog, Head of AI at Strongbytes:
