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v0.24.1
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  • ☂️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 0.24.1
      • Version 0.24.0
      • Version 0.23.0
      • Version 0.22.0
      • Version 0.21.0
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  1. NannyML Cloud SDK

Getting Started

Interact programatically with nannyML cloud throughout its SDK

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Last updated 4 months ago

NannyML Cloud SDK is a Python package that enables programmatic interaction with NannyML Cloud. It allows you to automate all aspects of NannyML Cloud, including:

  • Creating a model for monitoring.

  • Logging inferences for analysis.

  • Triggering model analysis

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

Installation

The nannyml-cloud-sdk package is available on PyPi and can be installed using your favorite package manager.

Compatibility

The following table shows which versions of the SDK are compatible with which versions of the NannyML Cloud product.

Authentication

After clicking the create button you'll be presented with a prompt to enter an optional description for the API token. We recommend describing what you intend to use the token for so you know which token to revoke later when you no longer need it. Copy the token from the prompt and store it in a secure location.

Once you have an API token you can use it to authenticate the NannyML Cloud SDK. Either by inserting the token & URL directly into the python code:

import nannyml_cloud_sdk as nml_sdk

nml_sdk.url = "https://beta.app.nannyml.com"
nml_sdk.api_token = r"api token goes here"

Or using environment variables:

import nannyml_cloud_sdk as nml_sdk
import os

nml_sdk.url = os.environ['NML_SDK_URL']
nml_sdk.api_token = os.environ['NML_SDK_API_TOKEN']

We recommend using an environment variable for the API token. This prevents accidentally leaking any token associated with your personal account when sharing code.

To use the NannyML Cloud SDK, you need to provide the URL of your NannyML Cloud instance and an API token to authenticate. You can obtain an API token on the page of your NannyML Cloud instance.

account settings
Link to account settings from the header
API tokens on account settings page
Create API token prompt
Generated token prompt
SDK version
NannyML Cloud version

>=0.1.6

>=0.24.1