It was a failure when I deployed my first Machine Learning model. Quite frankly, I was beyond excited when it was deployed — it was a simple Diabetes Diagnosis Model for potential diabetes mellitus patients. After receiving feedback from users, the excitement quickly dissipated. The model was not liked by the users. Although I was saddened by this, I now realize they were right. In terms of top-level metrics, the model may have performed well. However, from the perspective of the consumer, if a machine learning model provides a poor forecast, the consumer will have a negative experience with it. There was a problem with the model’s performance due to specific model features. Machine learning engineers must assess machine learning models before deploying them, ensure they meet strict quality standards, and ensure they behave as predicted for all relevant data slices. What is TensorFlow Model Analysis? In order to help Machine Learning engineers understand their models’ performance...
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