Model Monitoring for Enterprises

When employed properly, continuous ML monitoring accelerates the “time-to” factors that matter most.
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Monitoring chatbot models with Fiddler Trust Service
Industry Leaders’ Choice for AI Observability and Security
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.
Fiddler’s robust AI Observability platform is a powerful, yet flexible addition to our MLOps framework. Since using Fiddler, we have saved one day worth of troubleshooting for every issue that Fiddler detects. Fiddler has taken full care of our monitoring needs in the background, while the data science team is focusing on what matters in the foreground.
Suryanarayana Ambatipudi
Head of Data Science, Tide
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
Dashboard showing LLM applications monitoring charts
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.

Featured Resources

Guide
The Ultimate Guide to LLM Monitoring
Video
Compare and Analyze Cohorts Using Segment Monitoring
Podcast
Tracking Drift to Monitor LLM Performance
Case Study
Tide Drives Innovation, Scale, and Savings With AI Observability
Blog
Should Enterprises Observe Metrics or Inferences?
Blog
How to Monitor LLMOps Performance with Drift

Frequently asked questions, answered.

Still have more questions? Learn more in our docs or contact sales.

What is ML model monitoring?

ML model monitoring is the continuous tracking of machine learning models to assess performance, detect anomalies, and ensure reliability.

Why is monitoring machine learning models important?

Monitoring ML models is crucial to ensure they perform as expected in real-world conditions. It helps identify issues like data drift, bias, and model degradation to maintain accuracy over time.

What are the best practices for ML model monitoring?

Best practices include setting clear performance benchmarks, implementing real-time monitoring, regularly retraining models, and integrating automated alerts. Ensuring data quality and explainability also enhances monitoring effectiveness.

What metrics should be tracked in ML model monitoring?

Key metrics include accuracy, precision, recall, F1-score, AUC-ROC, data drift, model drift, latency, and error rates. Monitoring these ensures model reliability and helps detect potential performance declines.

How does ML model monitoring support AI governance?

Continuous monitoring provides transparency into model decision-making, ensuring accountability and compliance with ethical guidelines. It helps organizations maintain responsible AI practices and align with governance frameworks.

How can businesses measure the ROI of ML model monitoring?

Businesses can measure return on investment by tracking improvements in model accuracy, reduced downtime, operational efficiency, and risk mitigation.

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