<ul data-eligibleForWebStory="true"><li>A comparison of deploying a face mask classifier using TensorFlow, AWS Canvas, and Rekognition.</li><li>Methods included classic deep learning with TensorFlow, low-code/no-code with SageMaker Canvas, and Rekognition Custom Labels.</li><li>Evaluation criteria: ease of training/setup, deployment options, AWS pricing, computational cost & latency.</li><li>Results showed pros and cons of each method in terms of control, ease of use, cost, and portability.</li><li>Different approaches cater to various needs, emphasizing trade-offs in cost, control, and portability.</li></ul>

Comparing 3 ways to Deploy a Face Mask Classifier: Tensorflow, AWS Canvas, and Rekognition

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