WhyLabs AI Observatory

Cloud-agnostic platform for ML model monitoring.

Details

Freemium

Starts at $125/mo
December 15, 2023
Features
Scalability
Data Privacy
Observability
Best For
Data Scientist
AI Engineer
ML Operations Engineer
Data Engineer
Use Cases
Model Performance Monitoring
Data Quality Assurance
Risk Mitigation in Critical Shipments

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What is WhyLabs AI Observatory?

WhyLabs AI Observatory is a cloud-agnostic platform designed for massive-scale data monitoring and collaborative AI operations. It enables AI practitioners to monitor their machine learning (ML) models of any scale without any configuration required. The platform collects statistics, performance data, and metrics from all parts of an ML system, providing actionable insights to users. It uses the open-source whylogs library to log, test, and monitor ML applications without the need for raw data to leave the user’s environment. The feature data and model performance data are profiled in real-time, eliminating the need for data centralization and post-processing. With its scalable capabilities, it can monitor hundreds of models with thousands of features each, even if the models are making millions of predictions per hour. The platform also includes data quality monitors, model monitoring features, and ensures data privacy by processing data within the customer’s perimeter.

WhyLabs AI Observatory Features

  • Scalability

    Monitor hundreds of models with thousands of features, even at a high volume of millions of predictions per hour.

  • Data Privacy

    Keep customer data secure by processing it within the customer perimeter without the need to transfer it externally.

  • Observability

    Collect comprehensive statistics, performance data, and metrics from all aspects of the ML system for actionable insights.

  • Data Quality

    Detect changes in missing values, unique values, and inferred data types with data quality monitors.

WhyLabs AI Observatory Use Cases

  • Model Performance Monitoring

    Detect and monitor data drift, data quality issues, and performance degradation in ML models to ensure they are delivering accurate and reliable results in real-time.

  • Data Quality Assurance

    Use data quality monitors to identify changes in missing values, unique values, and data types, allowing for prompt remediation and ensuring the integrity of the data used for model training and inference.

  • Risk Mitigation in Critical Shipments

    Minimize risk throughout the supply chain for critical shipments by continuously monitoring and analyzing ML models that predict potential risks, enabling proactive intervention and avoiding costly incidents.

Related Tasks

  • Model Performance Monitoring

    Track and monitor the performance and behavior of machine learning models to ensure they consistently deliver accurate results.

  • Data Drift Detection

    Identify changes in data distribution over time to detect and address potential issues that may impact model performance.

  • Anomaly Detection

    Detect and flag anomalous behavior in ML models, enabling proactive troubleshooting and remediation.

  • Data Quality Monitoring

    Monitor and verify the quality of data used for training and inference, ensuring it remains consistent and reliable.

  • Prediction Analysis

    Analyze and visualize model predictions to gain insights into patterns, trends, and potential areas of improvement.

  • Feature Importance Assessment

    Evaluate the significance and impact of different features on model performance to inform feature engineering and optimization.

  • Error Analysis

    Investigate and diagnose errors or discrepancies in model outputs to refine and enhance the overall performance of ML models.

  • Performance Optimization

    Employ monitoring and analysis to identify bottlenecks and areas of inefficiency in ML models, optimizing their speed, accuracy, and resource utilization.

  • Data Scientist

    Utilizes WhyLabs AI Observatory to monitor the performance and data quality of machine learning models, ensuring accurate and reliable results.

  • AI Engineer

    Relies on WhyLabs AI Observatory to detect data drift, identify model performance issues, and optimize ML models for enhanced efficiency.

  • ML Operations Engineer

    Uses WhyLabs AI Observatory to continuously monitor and manage the performance and behavior of ML models in production environments.

  • Data Engineer

    Implements WhyLabs AI Observatory to ensure the quality and integrity of data used in ML models, identifying and addressing any issues or changes.

  • AI Researcher

    Leverages WhyLabs AI Observatory to assess the behavior and performance of experimental ML models, facilitating research and development efforts.

  • Data Analyst

    Utilizes WhyLabs AI Observatory to analyze and interpret data quality metrics, facilitating data-driven decision-making processes.

  • IT Architect

    Integrates WhyLabs AI Observatory into ML infrastructure and workflows, ensuring reliable monitoring and observability of ML models.

  • AI Product Manager

    Relies on WhyLabs AI Observatory to assess and track the performance and fitness of ML models for specific use cases, driving product improvements and optimizations.

WhyLabs AI Observatory FAQs

What is WhyLabs AI Observatory?

WhyLabs AI Observatory is a cloud-agnostic platform for massive-scale data monitoring and collaborative AI operations.

What are the key features of WhyLabs AI Observatory?

Key features include scalability, data privacy, observability, and data quality monitoring.

How does WhyLabs AI Observatory work?

It uses the whylogs library to log, test, and monitor ML applications without the need for raw data to leave the user's environment.

What are some use cases for WhyLabs AI Observatory?

Use cases include model performance monitoring, data quality assurance, and risk mitigation in critical shipments.

What is data privacy in WhyLabs AI Observatory?

Data privacy is ensured by processing data within the customer's perimeter, eliminating the need for data transfer.

What is the whylogs library?

The whylogs library enables logging, testing, and monitoring of ML applications without sharing raw data.

What is the scalability of WhyLabs AI Observatory?

WhyLabs AI Observatory can monitor hundreds of models with thousands of features, even at high prediction volumes.

What are data quality monitors in WhyLabs AI Observatory?

Data quality monitors detect changes in missing values, unique values, and inferred data types to ensure data integrity.

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