💡 What is NannyML Cloud SDK?

NannyML Cloud is a web application that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML Cloud has an easy-to-use interface, interactive visualizations, is completely model-agnostic and currently supports all tabular use cases, classification and regression.

NannyML Cloud SDK is a python package that enables programatic 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

🚀 Getting started

Install NannyML Cloud SDK

Currently the package is private, which means you cannot install it via the regular python channels. Instead, you'll have to clone the repository and install it from your local copy.

git clone https://github.com/NannyML/nannyml-cloud-sdk.git
cd nannyml-cloud-sdk
pip install .

Authentication

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 settings page of your NannyML Cloud instance.

In 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"

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']

Note

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

Quick start

This snippet provides an example of how you can create a model in NannyML Cloud to start monitoring it.

import nannyml_cloud_sdk as nml_sdk
import os
import pandas as pd

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

# Load a NannyML binary classification dataset to use as example
reference_data = pd.read_csv('https://github.com/NannyML/nannyml/raw/main/nannyml/datasets/data/synthetic_sample_reference.csv')
analysis_data = pd.read_csv('https://github.com/NannyML/nannyml/raw/main/nannyml/datasets/data/synthetic_sample_analysis.csv')
target_data = pd.read_csv('https://github.com/NannyML/nannyml/raw/main/nannyml/datasets/data/synthetic_sample_analysis_gt.csv')
print(reference_data.head())

# Inspect schema from dataset and apply overrides
schema = nml_sdk.monitoring.Schema.from_df(
    'BINARY_CLASSIFICATION',
    reference_data,
    target_column_name='work_home_actual',
    ignore_column_names=('period'),
)

# Create model
model = nml_sdk.monitoring.Model.create(
    name='Example model',
    schema=schema,
    chunk_period='MONTHLY',
    reference_data=reference_data,
    analysis_data=analysis_data,
    target_data=target_data,
    key_performance_metric='F1',
)
print("Model", model['id'], "created at", model['createdAt'])

Note

The reference dataset is inspected to determine the model schema. NannyML Cloud uses heuristics to automatically identify most columns, but some columns may not be automatically identified. In this case the target column is not identified, so we manually define work_home_actual as the target column.

Once a model has been set up in NannyML Cloud, you could use the snippet below to add more data and ensure continuous monitoring of your model.

import nannyml_cloud_sdk as nml_sdk
import os
import pandas as pd

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

# Find model in NannyML Cloud by name
model, = nml_sdk.monitoring.Model.list(name='Example model')

# Add new inferences to NannyML Cloud
new_inferences = pd.DataFrame()
nml_sdk.monitoring.Model.add_analysis_data(model['id'], new_inferences)

# If you have delayed access to ground truth, you can add them to NannyML Cloud
# later. This will match analysis & target datasets using an identifier column.
delayed_ground_truth = pd.DataFrame()
nml_sdk.monitoring.Model.add_analysis_target_data(model['id'], delayed_ground_truth)

# Trigger analysis of the new data
nml_sdk.monitoring.Run.trigger(model['id'])