NannyML

What it is

NannyML is a Python library that helps in capturing the impact of data drift on model performance after deployment. It allows users to estimate model performance without needing access to target labels, making it invaluable for continuous monitoring and maintenance of machine learning models.

NannyML stands out by offering a robust solution for estimating post-deployment model performance without the need for target labels, making it ideal for continuous monitoring in production environments.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Performance issues with large datasets

   Processing time increases exponentially with dataset size, impacting real-time monitoring capabilities

2. 02

   Documentation lacks depth and examples

   Core concepts are not well-explained, limited practical use cases provided in the documentation

What it does well

• Estimates model performance without target labels post-deployment.

• Detects data drift and its impact on model performance.

• Provides actionable insights for maintaining model accuracy.

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.