René's URL Explorer Experiment

{"@context":"https://schema.org","@type":"DiscussionForumPosting","headline":"Include connection to training/scoring data sources","articleBody":"The template should include realistic guidance on how to connect to data sources:\r\n- For training, how to connect to data sources (whether of a type supported by Azure ML Datasource classes, or other ODBC data source type)\r\n- Whether we should have a separate pipeline stage for pulling the data (separation of concerns + reuse of run steps) or just query the data in the training step\r\n- How to manage credentials, one way is to use Key Vault in the run script itself (https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.ipynb) but it's hard to find, should be part of the solution template (+any other method to manage credentials if appropriate)\r\n- For *scoring*, similarly how to manage connectivity to data sources. Enterprise DS models often enrich incoming requests with data from a data mart (e.g. user profiles)\r\n\r\n","author":{"url":"https://github.com/algattik","@type":"Person","name":"algattik"},"datePublished":"2019-09-27T08:53:58.000Z","interactionStatistic":{"@type":"InteractionCounter","interactionType":"https://schema.org/CommentAction","userInteractionCount":2},"url":"https://github.com/75/MLOpsPython/issues/75"}