Performance Monitoring | Superwise ML Observability

Platform
- Platform overview
  Take a dive into the Superwise model observability platform capabilities.
- ML observability
  Everything you need to observe ML system behaviors and keep your ML healthy in production.
- ML monitoring
  Easily create, customize & automate your ML monitoring with our library of metrics, policies & notification channels.
- Monitoring use cases
  - Data monitoring
  - Drift monitoring
- Model metric store
  Hit the ground running with 100+ pre-built & fully customizable metrics for data, drift, performance, bias, & explainability.
- Documentation
  Everything you need to get started with Superwise, from tutorials to recipes and API references.
- Elementa open-source
  Open-source library in Python letting you extract metafeatures from unstructured data.
- Request a demo
  Need some help getting started with model observability? Our team will walk you through everything you need to know.
Pricing
Learn
- Blog
  Learn how model observability can help you and your team monitor ML.
- Resources
  Whitepapers, use cases, and research. Everything you need effectively assure the health of your models in production.
- ML talks
  Leading ML practitioners from across the globe on what it takes to keep ML running smoothly in production.
- Troubleshooting model drift
- Drift in machine learning
  Everything you need to know about all types of drift including concept drift, data drift, and model drift.
- ML monitoring e-book
  A framework for building, testing, and implementing a robust model monitoring strategy.
- LLM garden
  Discover, search, compare & add LLMs to the garden.
- Model observability vs. software observability
Company
- About us
  Superwise is a Blattner Tech company. Click here to learn more
- News
  What’s new with Superwise.
- Events
  Join our webinars on ML observability and meet the teams at events across the globe.
- Elemeta: Extract metafeatures from unstructured data
- Careers
  Want to join the team? Check out our open positions!
- Contact us
  Need help getting started? Looking to colaborate? Contact us!
- Data drift detection basics

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Home
Platform
- Platform overview
  Take a dive into the Superwise model observability platform capabilities.
- ML observability
  Everything you need to observe ML system behaviors and keep your ML healthy in production.
- ML monitoring
  Easily create, customize & automate your ML monitoring with our library of metrics, policies & notification channels.
- Model metric store
  Hit the ground running with 100+ pre-built & fully customizable metrics for data, drift, performance, bias, & explainability.
- Documentation
  Everything you need to get started with Superwise, from tutorials to recipes and API references.
- Elementa open-source
  Open-source library in Python letting you extract metafeatures from unstructured data.
- Request a demo
  Need some help getting started with model observability? Our team will walk you through everything you need to know.
- Monitoring use cases
- Data monitoring
- Drift monitoring
- Performance monitoring
- NLP monitoring
- LLM monitoring
Pricing
Learn
- Blog
  Learn how model observability can help you and your team monitor ML.
- Resources
  Whitepapers, use cases, and research. Everything you need effectively assure the health of your models in production.
- ML talks
  Leading ML practitioners from across the globe on what it takes to keep ML running smoothly in production.
- Drift In machine learning
  Everything you need to know about all types of drift including concept drift, data drift, and model drift.
- ML monitoring e-book
  A framework for building, testing, and implementing a robust model monitoring strategy.
- LLM garden
  Discover, search, compare & add LLMs to the garden.
Company
- About us
  Superwise is a Blattner Tech company. Click here to learn more
- News
  What’s new with Superwise.
- Events
  Join our webinars on ML observability and meet the teams at events across the globe.
- Careers
  Want to join the team? Check out our open positions!
- Contact us
  Need help getting started? Looking to colaborate? Contact us!
Sign up / Log in
Book a demo

Performance monitoring

Define performance monitors for your use cases and track them continuously, no matter how short or long your feedback loop is.

Performance Monitoring Hero

Prediction shift

I want to monitor output drift for prediction in all LTV models across all segments

Identify changes in your model input-output relationship and pinpoint where assumed and real-world input-output pairs have diverged.

Get alerted when predictions shift on the segment level.
Identify increases and declines in prediction probability and confidence.

Performance degradation

I want to monitor distribution shift for prediction probability in risk model across all segments & entire set

Measure and monitor model performance continuously and detect drops or gradual changes over time before they impact your business and outcomes.

Measure from a variety of out-of-the-box performance metrics such as recall, accuracy, ROC, etc.
Create and monitor custom performance metrics that reflect account business KPIs.

Label shift

I want to monitor label shift for prediction in all CRO models across all segments

Monitor for changes between the true label distribution in your training data and the true label distribution in your collected ground truth.

Pinpoint true label changes on the sub-population level.
Receive alerts if labels are not collected as expected downstream.

Label Proportion

I want to monitor label proportion for prediction in 1 model across all segments

Track the distribution of labels or classes to ensure that poor performance isn’t going under the radar.

Monitor binary & multi-class classification problems.
Identify accuracy vs. performance issues.

Try the community edition

No credit card required.

1

Sign up for Superwise

Easily get started with a free community edition account.

!pip install superwise

2

Build your project

import superwise as sw

project = sw.project(“Fraud detection”)

model = sw.model(project,“Customer a”)

policy = sw.policy(model,drift_template)

3

Start monitoring

Fraud detection

Entire population drift – high probability of
concept drift. Open incident investigation →

Fraud detection

Segment “tablet shoppers” drifting.
Split model and retrain.

FREE community edition

Featured resources

Building your MLOps roadmap

Building your MLOps roadmap

Scaling up your model operations? in this blog we will offer some practical advice on how to build your MLOps roadmap

Building your MLOps roadmap

Putting together a continuous ML stack

Putting together a continuous ML stack

Due to the increased usage of ML-based products within organizations, a new CI/CD like paradigm is on the rise. On top of testing your code, building a package, and continuously deploying it, we must now incorporate CT (continuous training) that can be stochastically triggered by events and data and not necessarily dependent on time-scheduled triggers.

Putting together a continuous ML stack

Build or buy

Build or buy? Choosing the right strategy for your model observability

If you’re using machine learning and AI as part of your business, you need a tool that will give you visibility into the models that are in production: How is their performance? What data are they getting? Are they behaving as expected? Is there bias? Is there data drift? Clearly, you can’t do machine learning

Build or buy? Choosing the right strategy for your model observability

Platform

ML observability

Model metric store

Learn

Drift in machine learning

ML monitoring e-book

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