My ML Model Returned HTTP 200 on Every Request. It Was Still Wrong.

1. 1Deploy a model to a production environment using a framework like TensorFlow or PyTorch
2. 2Monitor the model's performance using metrics such as accuracy, precision, and recall
3. 3Test the model with various input scenarios to identify potential biases or errors
4. 4Analyze the model's logs to detect any issues or anomalies
5. 5Compare the model's performance with expected results to identify discrepancies

Data scientists and machine learning engineers can benefit from understanding the differences between model deployment and actual performance, ensuring their models work as expected in production

💡 Model deployment and model performance are not the same thing, and monitoring is crucial to ensure accuracy

🚨 HTTP 200 doesn't mean your ML model is working correctly! 🚨