ML Integrity

ML Integrity is a set of practices that aims to eliminate risk throughout the ML lifecycle through data quality, model performance, fairness, security, and transparency.

What is ML Integrity?

Models that work as intended

Machine Learning Integrity (MLI) is a set of practices that aims to eliminate risk throughout the ML lifecycle through data quality, model performance, fairness, security, and transparency. It is necessary to think about MLI at each stage of the ML lifecycle - from data preparation to model development to model operations. Failures in any one area can have critical downstream consequences for your business and customers.
Data Quality
Model Performance
Companies that adopt AI also adopt AI risk. ML has become an important tool in an engineer’s toolkit to meet a company’s automation requirements. Yet, despite its widespread use to make business-critical decisions, ML fails frequently and in surprising ways. While it has become easier to develop and deploy ML systems, there is no standard way to validate them and ensure that they can handle bad inputs, edge cases, and unknowns.

As the number of models in production increases, so does the risk. ML Integrity enables companies to greatly minimize the chance of model failure and thereby unleash the true potential of AI.

What are the three primary benefits of ML Integrity?


Substantially reduces business risk

ML models that fail in production introduce a myriad of risks to the business including lost revenue due to silent errors, reputational damage from biased models, lawsuits from impacted users, and fines resulting from non-compliance. The high impact of such incidents has forced organizations to consider how they can implement processes to achieve ML Integrity. By introducing MLI at each stage of the ML lifecycle, companies can substantially reduce instances of these model failures.

Accelerates model velocity

Organizations are continually finding new applications for ML, however, a lack of clearly defined standards and a reactive posture make it difficult to scale models in production. Data science leaders recognize that ensuring models conform to a standard definition of production readiness and adopting proactive risk mitigation measures are key to increasing model velocity. These MLI practices give teams the confidence to innovate and deploy models to production at a faster rate.

Saves engineering resources

Traditional practices of model development and monitoring are resource-intensive, requiring ad hoc testing and time-consuming investigation into model failure alerts. This takes considerable time from data scientists and ML engineers that could otherwise be better spent. ML Integrity enables ML teams to automate the testing process and implement proactive measures to protect models in production. These practices can help put an end to 2 AM firefighting calls and endless hours tuning thresholds and staring at dashboards.

How does ML Integrity work with MLOps?

The huge growth in MLOps in recent years has empowered organizations to build and deploy machine learning systems at an accelerated rate. While pushing models into production has gotten easier, understanding and trusting these systems has only gotten harder. Organizations that wish to scale with AI are facing new challenges in AI risk. ML Integrity allows organizations to continue to scale with the ease of MLOps, yet with guardrails and protective measures in place for when ML models act in unpredictable ways.

The Robust Intelligence approach to ML Integrity.

From data exploration and model development to operationalizing models and retraining, Robust Intelligence provides a proactive, end-to-end solution for ML Integrity. Every AI risk symptom, such as bias, bad input, drift or adversaries, can be measured and mitigated by an appropriately designed test.
Pre-deployment: AI Stress Testing provides hundreds of auto-configured and customizable tests that can identify implicit assumptions and model failures, allowing you to harden against these vulnerabilities.
Post-deployment: AI Firewall protects production models from data that can cause erroneous predictions and failure. AI Continuous Testing monitors the behavior of models in production to identify issues, informs you when it’s time to retrain a model, and automates root cause analysis of model failure.
Robust Intelligence instills ML Integrity in models by profiling datasets during development to verify data quality, providing suggestions of ways to improve model performance by detecting drift and underperforming subsets, running a rigorous set of bias tests over protected features to ensure fairness, improving security with simulated attacks stress testing and real-time alerting to adversarial inputs, and assuring business transparency with a standardized and data-centric approach allow users to monitor test runs and create regular reports.