Detect, prevent, and mitigate
risk in your AI applications
- Start with reliable data. Continuously monitor any data-in-motion for data quality issues.
- Pinpoint data and model drift. Identify training-serving skew and proactively retrain.
- Detect model accuracy degradation by continuously monitoring key performance metrics.
- Identify risky behavior in generative AI applications and prevent data leakage.
- Protect your generative AI applications are safe from malicious actions.
- Improve AI applications through user feedback, monitoring, and cross-team collaboration.
Structured or unstructured. Monitor raw data, feature data, predictions and actuals.
Batch or streaming. Integrate seamlessly with existing data pipelines and multi-cloud architectures.
Go from massive amounts of data to real-time actionable insights in minutes.
What leading AI teams are saying about WhyLabs
“We chose WhyLabs for several reasons. First, they provide all the core model monitoring functionalities that we're looking for including a straightforward presentation of results, outlier detection, histograms, data drift monitoring, and missing feature values. [Second,] they have strong data privacy due to their aggregation of data before consumption and very fast ingestion.”
ML Platform Program Manager
Fortune 500 Fintech
“At Airspace, we use AI to minimize risk across the supply chain for the world's most critical shipments. WhyLabs has been instrumental in driving the scalability of our AI operations. The platform offers easy onboarding, data privacy-friendly integration, and a command-center view that allows us to quickly identify and treat problems before they impact the user experience. The downstream impact of enabling observability is that we are able to continuously expand on our differentiating technology by leveraging machine learning for more use cases”
Co-founder and CTO, Airspace
### First, install whylogs with the whylabs extra ### pip install -q 'whylogs[whylabs]' import pandas as pd import os import whylogs as why os.environ["WHYLABS_API_KEY"] = "YOUR-API-KEY" os.environ["WHYLABS_DEFAULT_ORG_ID"] = "YOUR-ORG-ID" os.environ["WHYLABS_DEFAULT_DATASET_ID"] = "model-1" # Note: the 'model-id' is provided when setting-up a model in WhyLabs # Point to your local CSV if you have your own data df = pd.read_csv("https://whylabs-public.s3.us-west-2.amazonaws.com/datasets/tour/current.csv") # Run whylogs on current data and upload to the WhyLabs Platform results = why.log(df) results.writer("whylabs").write()