The promise of AutoML training is to make machine learning accessible to the everyday predictor, but the reality is that each system has different levels of complexity and cost to use. The high barrier to entry has resulted in a distinct lack of performance benchmarking.
We tested Google Cloud AI, Microsoft Azure AutoML, Amazon Sagemaker Autopilot, and Akkio on a number of open source real-world datasets. Models were benchmarked based on achieved accuracy and F1 scores, as well as training time and cost.
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