Validate the data behind your AI systems

AI data validation

Validate the data behind your AI systems

Openlayer helps teams validate datasets before training or inference—catching errors, drift, and anomalies early.

Why data validation matters for AI

Bad data = bad models

Even the most advanced models fail when trained or evaluated on poor-quality data.

Openlayer helps you test and validate data at the source

Make sure your models never start from flawed inputs. Issues like null values, schema mismatches, distribution drift, or unbalanced classes can silently degrade model performance.

What AI data validation should include

Beyond profiling, toward continuous validation

What does a modern AI data validation platform look like?

Schema and column validationConfirm every table, field, and data type before downstream jobs break on unexpected changes.

Distribution and range checksCatch subtle drift by comparing histograms, percentiles, and value ranges against strict baselines.

Null and anomaly detectionSurface unexpected nulls, outliers, or sudden spikes in real time so bad records never slip through.

Integration with data lakes and pipelinesPlug into Databricks, Snowflake, BigQuery, or other workflows—no data copying or re-platforming.

Triggered validations for every batch or streamRun the full test suite automatically on each ingest or micro-batch so data stays clean around the clock.

Openlayer's approach

Automated validation built into the pipeline

Run validation tests directly on Snowflake, BigQuery, S3, and morePoint Openlayer at your data source and execute schema, drift, and quality checks in place—no exports, no duplications.

Auto-detect anomalies across structured, unstructured, and multimodal dataWhether it’s a missing column, a blurry image, or an off-topic text chunk, Openlayer spots deviations the moment they appear.

Visualize test outcomes, flag issues, and alert your team instantlyDashboards show pass/fail status at a glance while Slack, PagerDuty, or email alerts ensure the right people jump in fast.

No heavy data engineering requiredAdd a few lines of configuration—Openlayer handles the orchestration, storage, and scaling behind the scenes.

FAQs

Your questions, answered

What is AI data validation?It's the process of testing and verifying data used to train, evaluate, or serve AI systems to ensure its integrity and quality.

How is this different from data profiling?Validation actively tests your data against expected patterns and thresholds, rather than just describing it.

Does Openlayer support real-time validation?Yes, validations can be triggered continuously or on a scheduled basis.

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AI data validation