Take a dive into the Superwise model observability platform capabilities.
Everything you need to observe ML system behaviors and keep your ML healthy in production.
Easily create, customize & automate your ML monitoring with our library of metrics, policies & notification channels.
Hit the ground running with 100+ pre-built & fully customizable metrics for data, drift, performance, bias, & explainability.
Everything you need to get started with Superwise, from tutorials to recipes and API references.
Open-source library in Python letting you extract metafeatures from unstructured data.
Need some help getting started with model observability? Our team will walk you through everything you need to know.
Learn how model observability can help you and your team monitor ML.
Whitepapers, use cases, and research. Everything you need effectively assure the health of your models in production.
Leading ML practitioners from across the globe on what it takes to keep ML running smoothly in production.
Everything you need to know about all types of drift including concept drift, data drift, and model drift.
A framework for building, testing, and implementing a robust model monitoring strategy.
Discover, search, compare & add LLMs to the garden.
Who we are, how we got here, and where we’re going.
What’s new with Superwise.
Join our webinars on ML observability and meet the teams at events across the globe.
Make a Superwise move and join our team.
Need help getting started? Looking to colaborate? Contact us!
Makes sure that your models are reliable and that your top features contribution to ML predictions remains consistent.
Detect distribution discrepancies between the data used to train your machine learning model and the data encountered in production.
Monitor high dimensionality scenarios with dataset shift monitoring to quantify drift for the entire dataset together and drill down to understand precisely what is drifting and where it’s drifting.
!pip install superwise
import superwise as sw
project = sw.project(“Fraud detection”)
model = sw.model(project,“Customer a”)
policy = sw.policy(model,drift_template)
Entire population drift – high probability of concept drift. Open incident investigation →
Segment “tablet shoppers” drifting.
Split model and retrain.