Join us for the latest AI Explained: Metrics to Detect Hallucinations with RagaAI on 4/25. Register

Model Monitoring

Everyone Needs Model Monitoring

When employed properly, continuous ML monitoring accelerates the “time-to” factors that matter most.
Request demo

Gain efficiencies

Attain faster time-to-market at scale with more models in production

Reduce costs

Achieve faster time-to-value with decreased errors and resolution time

Improve team alignment

Eliminate silos and save on hours across teams with quicker time-to-resolution

"With Fiddler, I am always in control of my machine learning models and it's a great relief to not need to constantly check on our production models. Fiddler alerts us whenever the performance of our models drops or the incoming data has a different distribution than our training set."
Richard Sieg
Lead Data Scientist, how.fm
Fiddler AI monitor data integrity
Gain efficiencies

Never let monitoring or anomalies slow you down

Want to decrease time-to-production and increase the number of models you release? How about resolving ML model issues with speed so you can achieve faster time-to-market?

The centralized dashboard delivers deep insights into model behavior and uncovers data pipeline issues to save debugging time. The Fiddler AI Observability platform operates at enterprise scale so you can go to market faster by monitoring and validating models during pre-deployment phases and releasing them in production.

  • Catch and fix model inference violations right when they happen.
  • Detect outliers and quickly assess which ones are caused by specific model inputs.
  • Pinpoint data drift and contributing features to know when and how to retrain models. 
Reduce costs

Boost ML models with integrated monitoring

Adoption of an enterprise-scale solution saves you the ongoing costs associated with building and managing an in-house ML monitoring solution. More importantly, it saves engineering and data scientists’ time so they can focus on what they do best: building ML models.

Reducing errors saves money, and delivering high-performance models to customers increases satisfaction and referrals.

  • Lower costs by reducing mean time for issue identification and resolution.
  • Decrease the number of errors to save money and engineering time.
  • Grow revenue by increasing the number of models put in production in less time.
Fiddler AI monitor data drift
Improve team alignment

Leverage teamwork and talent with one-stop monitoring

If you have process silos and disparate monitoring solutions, you are at risk of operational inefficiencies, not to mention losing out on the benefits associated with collaboration.

Monitor all training and deployed models in one place for streamlined detection of data changes. The Fiddler intelligent platform empowers teams to come together, discover, discuss, and fix issues.

  • Deliver a common platform with defined terminology to work across different MLOps teams.
  • Enable multiple teams to work on and use a single model at the same time.
  • Provide a unified dashboard with shared insights and custom real-time alerting.
  • Optimize business outcomes by connecting model performance metrics to business KPIs.

Model monitoring features

Performance monitoring

Track your model’s performance and accuracy with out-of-the-box metrics, including binary classification, multi-class classification, regression, and ranking models

Artifact monitoring

True model monitoring needs artifact monitoring. Fiddler monitors your model and its artifacts.

Data drift

Easily monitor data drift, uncover data integrity issues, and compare data  distributions between baseline and production datasets to boost model performance

Prediction drift and impact

Use popular drift metrics like Jensen-Shannon Divergence (JSD) and Population Stability Index (PSI) to uncover any data drift and help calculate which drifting features are impacting your model’s accuracy

NLP and CV monitoring

Increase prediction accuracy by monitoring complex and unstructured data, such as natural language processing and computer vision, in your models

Class imbalance

Detect changes in low-frequency predictions due to class imbalance in each stage of your ML workflow

Feature quality

Uncover data integrity issues in your data pipeline, including missing feature values, data type mismatches, or range violations

Ground truth updates

Update ground truth labels in a delayed, asynchronous fashion

Alerts

Configure and receive real-time alerts to identify and troubleshoot high-priority issues caused by performance, data drift, data integrity, and traffic metrics

Ready to try AI Observability?
Request demo
Contact us