From dfbfee6a48c7016e035243860bc2f0d8381f6e5e Mon Sep 17 00:00:00 2001 From: drcrook1 Date: Tue, 2 Apr 2019 08:45:57 -0400 Subject: [PATCH] more images; checking to see how it looks --- README.md | 16 ++++++++-------- readme_images/add_ml_workspace.png | Bin 0 -> 18381 bytes readme_images/adls_gen2_advanced_settings.png | Bin 0 -> 34937 bytes readme_images/adls_gen2_basic_settings.png | Bin 0 -> 67884 bytes ..._ml_workspace_resource_creation_settings.png | Bin 0 -> 13045 bytes readme_images/select_storage_account.png | Bin 0 -> 38373 bytes 6 files changed, 8 insertions(+), 8 deletions(-) create mode 100644 readme_images/add_ml_workspace.png create mode 100644 readme_images/adls_gen2_advanced_settings.png create mode 100644 readme_images/adls_gen2_basic_settings.png create mode 100644 readme_images/populate_ml_workspace_resource_creation_settings.png create mode 100644 readme_images/select_storage_account.png diff --git a/README.md b/README.md index 71ae114..52aacb2 100644 --- a/README.md +++ b/README.md @@ -15,20 +15,20 @@ Begin by creating 3 resource groups. These steps should be completed for resource groups [some-name]-db-dev and [some-name]-db-pipeline. [some-name]-db-prod will have different resources completely. #### Add Machine Learning Service Workspace 1. Select “Add a Resource”. - ![alt text](./readme_images/add_resource.png) +![alt text](./readme_images/add_resource.png) 2. Search for “machine learning” and select “Machine Learning service workspace” published by Microsoft. Click Create -a. +![alt text](./readme_images/add_ml_workspace.png) 3. Populate the fields with a naming convention that makes sense to you. Select the correct resource group and ensure the location pairs with your other services. -a. +![alt text](./readme_images/populate_ml_workspace_resource_creation_settings.png) #### Add Data Lake (Azure Storage gen 2) 1. Select “Add a Resource” form within a resource group pane. -a. +![alt text](./readme_images/add_resource.png) 2. Search for “Storage” and select “Storage account” and click “create” -a. +![alt text](./readme_images/select_storage_account.png) 3. Fill out the creation form. Ensure you are in the correct resource group. Give the account a name, ensure it is StorageV2 and set access tier to Cool. -a. +![alt text](./readme_images/adls_gen2_basic_settings.png) 4. Click on “Advanced” and ensure “Hierarchical namespace” under “Data Lake Storage Gen2” is selected as “enabled”. -a. +![alt text](./readme_images/adls_gen2_advanced_settings.png) 5. Select Create #### Add Azure Key Vault 1. Select “Add a Resource” from within a resource group pane. @@ -202,7 +202,7 @@ d. Use “Project_One/notebooks/train_model.py” as the path in git repo. The dev loop experience encompasses mounting the dev data, exploring that data, training a model; writing the inference code, compiling a dev container; running tests inside the dev container. ## Train the world’s worst regression & Stage for inference coding. 1. Copy the code from Project_One/notebooks/train_model.py into your databricks train_model.py which was created earlier. -2. The proctor will step through what exactly the code is doing and why. +1. The proctor will step through what exactly the code is doing and why. 1. Essentially: The precreated secrets are being used to mount to various stores securely and will allow zero code changes as the algorithm progresses across secure environments. 2. You train a super simple algorithm and register the resulting model files with the AZML service such that we can bridge the divide between databricks and inference coding. 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