NannyML Cloud
HomeBlogNannyML OSS Docs
v0.24.3
v0.24.3
  • ☂️Introduction
  • Model Monitoring
    • Quickstart
    • Data Preparation
      • How to get data ready for NannyML
    • Tutorials
      • Monitoring a tabular data model
      • Monitoring with segmentation
      • Monitoring a text classification model
      • Monitoring a computer vision model
    • How it works
      • Probabilistic Adaptive Performance Estimation (PAPE)
      • Reverse Concept Drift (RCD)
    • Custom Metrics
      • Creating Custom Metrics
        • Writing Functions for Binary Classification
        • Writing Functions for Multiclass Classification
        • Writing Functions for Regression
        • Handling Missing Values
        • Advanced Tutorial: Creating a MTBF Custom Metric
      • Adding a Custom Metric through NannyML SDK
    • Reporting
      • Creating a new report
      • Report structure
      • Exporting a report
      • Managing reports
      • Report template
      • Add to report feature
  • Product tour
    • Navigation
    • Adding a model
    • Model overview
    • Deleting a model
    • Model side panel
      • Summary
      • Performance
      • Concept drift
      • Covariate shift
      • Data quality
      • Logs
      • Model settings
        • General
        • Data
        • Performance settings
        • Concept Drift settings
        • Covariate Shift settings
        • Descriptive Statistics settings
        • Data Quality settings
    • Account settings
  • Deployment
    • Azure
      • Azure Managed Application
        • Finding the URL to access managed NannyML Cloud
        • Enabling access to storage
      • Azure Software-as-a-Service (SaaS)
    • AWS
      • AMI with CFT
        • Architecture
      • EKS
        • Quick start cluster setup
      • S3 Access
    • Application setup
      • Authentication
      • Notifications
      • Webhooks
      • Permissions
  • NannyML Cloud SDK
    • Getting Started
    • Example
      • Authentication & loading data
      • Setting up the model schema
      • Creating the monitoring model
      • Customizing the monitoring model settings
      • Setting up continuous monitoring
      • Add delayed ground truth (optional)
    • API Reference
  • Probabilistic Model Evaluation
    • Introduction
    • Tutorials
      • Evaluating a binary classification model
      • Data Preparation
    • How it works
      • HDI+ROPE (with minimum precision)
      • Getting Probability Distribution of a Performance Metric with targets
      • Getting Probability Distribution of Performance Metric without targets
      • Getting Probability Distribution of Performance Metric when some observations have labels
      • Defaults for ROPE and estimation precision
  • Experiments Module
    • Introduction
    • Tutorials
      • Running an A/B test
      • Data Preparation
    • How it works
      • Getting probability distribution of the difference of binary downstream metrics
  • miscellaneous
    • Engineering
    • Usage logging in NannyNL
    • Versions
      • Version 0.24.3
      • Version 0.24.2
      • Version 0.24.1
      • Version 0.24.0
      • Version 0.23.0
      • Version 0.22.0
      • Version 0.21.0
Powered by GitBook
On this page
  • Using role assignments
  • Setting permissions on your storage account
  • Setting up the data source in NannyML Cloud
  1. Deployment
  2. Azure
  3. Azure Managed Application

Enabling access to storage

How to ensure NannyML can access data stored in Azure Storage

PreviousFinding the URL to access managed NannyML CloudNextAzure Software-as-a-Service (SaaS)

Using role assignments

As a part of the deployment process, NannyML Cloud creates a managed identity aptly called nannyml.

By granting that managed identity the correct roles and permissions, you can have the NannyML Cloud instance read data from a storage container!

Setting permissions on your storage account

  1. Navigate to your storage account using the Azure portal. In this example, we have a storage account with a container called model-monitoring. The access level has been set to private. There are three files present, representing reference data, analysis data, and target data.

  2. Navigate to the Access Control (IAM) pane.

  3. Now click the Add role assignment button in the bottom left corner.

  4. This window will give you a very long overview of available roles. You can select any applicable role here, but something like Blob Data Reader should give sufficient permissions to read the data in this storage container. Search for the role using the search bar, select it and hit the Next button in the bottom left corner.

  5. Now you'll select the member to assign the role to. In this case you'll assign it to a managed identity, so select that option. Then hit the + Select members link to open up the search pane.

  6. In the search pane, first, select the subscription under which the NannyML Cloud managed application was deployed. In the Managed identity dropdown, select the User-assigned managed identity option. Finally, in the search bar under the Select header, filter for nannyml and select the correct option.

  7. Confirm the selected member and hit the Select button.

  8. Now click the Review + assign button in the bottom left to create the role assignment.

Setting up the data source in NannyML Cloud

Now you can use the details of the storage container to access your data within NannyML cloud.

  1. Set up a new model in NannyML Cloud. Select the Upload via Azure Blob Storage option.

  2. Now provide the details about the storage container we've just tweaked the access control for. Note that we don't have to provide any kind of authentication token or key.

  3. We now have access to the file!

Accessing the access control settings for our storage container
Navigating to the role assignment screen
Selecting a role to assign
This is the managed identity you're looking for
Searching for the correct managed identity
Confirming once more
Finally, let's assign the role!
Using Azure Blob Storage
Look mom, no credentials!
All file details are available!