diff --git a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb index adcf2463..be59751c 100644 --- a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb +++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb @@ -57,7 +57,7 @@ "1.\tDevelop a machine learning script in Python which involves the training script and the explanation script.\n", "2.\tCreate and configure a compute target.\n", "3.\tSubmit the scripts to the configured compute target to run in that environment. During training, the scripts can read from or write to datastore. And the records of execution (e.g., model, metrics, prediction explanations) are saved as runs in the workspace and grouped under experiments.\n", - "4.\tQuery the experiment for logged metrics and explanations from the current and past runs. Use the interpretability toolkit\u00e2\u20ac\u2122s visualization dashboard to visualize predictions and their explanation. If the metrics and explanations don't indicate a desired outcome, loop back to step 1 and iterate on your scripts.\n", + "4.\tQuery the experiment for logged metrics and explanations from the current and past runs. Use the interpretability toolkit’s visualization dashboard to visualize predictions and their explanation. If the metrics and explanations don't indicate a desired outcome, loop back to step 1 and iterate on your scripts.\n", "5.\tAfter a satisfactory run is found, create a scoring explainer and register the persisted model and its corresponding explainer in the model registry.\n", "6.\tDevelop a scoring script.\n", "7.\tCreate an image and register it in the image registry.\n", @@ -595,4 +595,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +}