NannyML Cloud
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v0.24.2
v0.24.2
  • ☂️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 0.24.2
      • Version 0.24.1
      • Version 0.24.0
      • Version 0.23.0
      • Version 0.22.0
      • Version 0.21.0
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On this page
  • Prerequisites
  • Finding the NannyML Cloud managed application
  • Deploying and customizing your instance
  • Basics settings
  • Infrastructure settings
  • Authentication settings
  • Just-In-Time (JIT) access
  • Review and create
  1. Deployment
  2. Azure

Azure Managed Application

Deployment instructions for NannyML Cloud as a managed application on Azure

PreviousAzureNextFinding the URL to access managed NannyML Cloud

NannyML Cloud is available on the Azure marketplace as a managed application. It will provision the NannyML Cloud components and the required infrastructure within your own subscription. This page provides deployment instructions and describes the available configuration options to customize your NannyML Cloud instance.

Prerequisites

  • Have an Azure account with an active .

If you prefer a video walkthrough, here's our Azure Setup YouTube guide:

Finding the NannyML Cloud managed application

To deploy the NannyML Cloud managed application, we'll first have to find it in the Azure marketplace.

  1. Search for Marketplace and select it from the available options. Or if you've recently used Marketplace, select it from your recently used services on top.

  2. On the Marketplace page, search for NannyML Cloud. You might notice two results: one for our Software-as-a-Service (SaaS) offer and one for our managed application offer. Make sure to select the one marked as Managed Application. Click the highlighted result or the Create button located at the bottom.

  3. Review the product and pricing information. Note that we currently only offer the NannyML Cloud managed application with a Professional plan. When you're ready, click the Create button to start the deployment wizard.

Deploying and customizing your instance

Basics settings

In this first form, you'll have to configure some Azure basics:

  1. Subscription: use the dropdown box to select an eligible subscription. The NannyML managed application will be billed using the billing methods associated with this subscription.

  2. Resource group: select an existing or create a new resource group to house the managed application resource. The managed application itself will create two more resource groups to house the internal infrastructure. See the Engineering page for more details.

  3. Region: select a region supported by Azure to house your managed application resources.

  4. Application name: choose a name for your managed application.

  5. Managed resource group: you can set a name for the resource group that will be created to hold the infrastructure created by the managed application. A value will be suggested for you, so setting this manually is optional.

Infrastructure settings

NannyML Cloud is running on a Kubernetes cluster under the covers. You can tweak some of the cluster settings that directly impact the infrastructure cost.

  1. Node count type: you can select one of two options here.

    1. Auto-scaled: this will enable the cluster hosting NannyML cloud to scale dynamically according to its workload.

    2. Fixed: this will run the cluster on a fixed amount of nodes.

  2. Provide node count values, depending on the option selected in step 1.

    • Minimal node count: the minimal amount of nodes your cluster will scale to. It should be at least 1.

    • Maximal node count: the maximal amount of nodes your cluster will scale to.

    • Node count: the fixed number of nodes for your cluster in case.

  3. Node size: choose a size for the virtual machine that will host your Kubernetes node. Take into account that the Kubernetes processes require some resources as well.

  4. Domain name label: provide a label used to expose your application. NannyML will be available at the following URL after provisioning completes: https://<domain-name-label>.<region>.cloudapp.azure.com. A full example would be https://nannyml-managed.westeurope.cloudapp.com.

The defaults for Kubernetes cluster node count and size are our recommendations for small to medium workloads consisting of up to 10 models. Auto-scaling is recommended as it provides a more future-proof option. When trying out NannyML Cloud a fixed single node is sufficient.

The domain name label must be unique across your region. The form will enforce this.

Authentication settings

There are three options for authentication in NannyML Cloud:

  1. Basic authentication: this setting allows you to provision your own users by specifying an email address and associated password.

    1. Audience: the identifier of the application that was registered in Azure AD. Denoted as the appid

    2. Tenant ID: the identifier for the tenant where the application was registered for in Azure AD

  2. None: don't have any authentication enabled at all. Navigating to the managed NannyML Cloud instance will take you directly into the application.

Just-In-Time (JIT) access

The following section allows you to set up and configure Just-In-Time (JIT) access. Since NannyML Cloud is provisioned as a managed application, the NannyML support team has direct access to the infrastructure that has been created. This will only be used during support interventions or remote updates.

In case you'd like to restrict the automatic access, you can enable JIT access. This allows the NannyML Cloud support team to launch a request for access that you can then approve for a period of time. Only then can your managed NannyML Cloud instance be accessed by a remote NannyML support team.

You, the customer, do not have access to the underlying infrastructure of NannyML Cloud.

Disabling JIT access does NOT revoke access for the NannyML support team. It merely indicates that you don't require the NannyML support team to explicitly request access.

Review and create

Now, you can review the selected plan and deployment options. Any required configuration values that are missing will be indicated in the wizard. You'll be asked to agree to the publisher, the NannyML support team, having access to the infrastructure provisioned on your subscription.

After agreeing to publisher access and fixing any invalid configuration values, the Create button should now be enabled. After clicking it, the deployment begins. You can track progress on the dedicated deployment page. Once it finishes, you'll see a notification appear at the top of the browser window or in your notification overview.

Sign in to the Azure .

Azure Entra (Active Directory): this setting allows you to integrate NannyML Cloud with Azure Entra for authentication. This requires in Azure Entra.

To enable JIT access, you should have an . Enabling JIT without having this license will severely disrupt the support flow.

portal
registering NannyML Cloud as an application
Azure Entra P2 license
subscription
Finding the Azure marketplace
Finding NannyML Cloud in the marketplace
Starting the creation wizard
Configuring basic settings
Configuring infrastructure
Options for a fixed node count
Provisioning users for basic authentication
Setting up integration with Azure Entra (Active Directory)
Agreeing to publisher access
Waiting for the deployment