From the course: Data Science on Google Cloud Platform: Predictive Analytics
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Performance monitoring - Google Cloud Tutorial
From the course: Data Science on Google Cloud Platform: Predictive Analytics
Performance monitoring
- [Instructor] So, you have built and deployed a model. That's great. Now, you need to see how it's doing. Cloud ML provides a user interface to monitor performance of models in real time. Go to the models page, and select the model. Then select the version, and scroll down the page. You will see performance data for this specific version. You can choose a chart interval based on the interval for which you want to analyze performance. Predictions per second shows the incoming prediction load requests. This tells you how much load is being put on the model. Prediction errors keep track of errors that happen during the prediction process. If there is any code-related errors, or data-related errors, it is going to show up here. Requests per second contains the number of requests coming in. Each request might contain multiple predictions. Response codes indicate the HTTP response code, returned through the prediction process. A 200 OK response code indicates success. Other codes need to…
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