📄️ Feature Walkthrough
WhyLabs Observe provides full AI lifecycle observability for insights into your data and model health, alerting you to drift events, performance degradations, potential attacks, and model behavior changes. All model types including large language models (LLMs) are supported, as are all data types including structured, unstructured, and streaming data.
🗃️ Profiles
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🗃️ Monitoring
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📄️ Notifications and Actions
Overview
📄️ Metrics overview
WhyLabs Platform makes it easy to track model and data health across all essential AI use cases. This page is a big overview of the different types of metrics you can track in the platform along with links to understand these metrics further.
📄️ Performance Metrics
In addition to profiling inputs and outputs of ML models, WhyLabs can automatically track a variety of model performance metrics. This is true even for delayed or partial ground truth, a common scenario in production ML systems. This means that at any point of time we can upload or update the model's performance metrics by assigning the appropriate timestamp.
📄️ Segmenting Data
Overview
📄️ Model Explainability
Maintaining explainability throughout a model’s life cycle is becoming increasingly important for running responsible ML applications. The WhyLabs AI Observatory makes this possible by helping you understand why and how a model is producing its output for both the global set of input data as well as for subsets of input data over time.
🗃️ Logging
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📄️ Model Lifecycle
AI Observatory allows users to enable important features by integrating with their model lifecycle tracking process. Users can easily enable model lifecycle tracking if they are using MLflow, Metaflow, etc.
🗃️ Root Cause Analysis
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