Benchmarking the Major Cloud Vision AutoML Tools

Jacob Solawetz's blog post provides a comprehensive analysis of three major cloud providers' no-code tools for training custom object detection models: Amazon Rekognition Custom Labels, Azure Custom Vision, and Google Cloud AutoML Vision. The post evaluates these platforms based on factors like training time, costs, model performance, and inference time and costs, comparing them against the open-source YOLOv5 model. The evaluation reveals that while open-source models can perform on par with these cloud services, each platform presents unique strengths and limitations, such as proprietary APIs, ease of use, and varying cost structures. For those with small budgets seeking to explore computer vision, Amazon Rekognition is recommended, while those with larger budgets may benefit from using Roboflow to test all platforms and optimize for performance or sustainable long-term costs. The blog underscores the importance of considering project-specific requirements, such as inference speed and budget, and suggests Roboflow Train as an alternative solution for deploying state-of-the-art models.