Openlayer
UnknownAI governance and observability platform for evaluating, monitoring, testing, and securing ML, LLM, and agentic systems.
Информация об инструменте
Что такое Openlayer?
Openlayer is a unified platform for continuous evaluation, observability, real-time monitoring, data quality checks, experiment tracking, and automated compliance. It supports generative AI, LLMs, traditional ML models, and AI agents from development through production, helping teams build trustworthy systems with full visibility and control.
Основные функции
LLM observability
Provides full visibility into LLM pipelines by tracing system behavior, monitoring cost and latency, and catching failures before users are impacted.
LLM evaluation
Enables rigorous evaluation of GenAI and LLMs by testing prompts, tracing responses, and validating outputs with customizable tests.
ML observability
Continuously tracks performance, drift, and live issues with automated observability for traditional ML systems.
ML evaluation
Evaluates ML models with 100+ customizable tests, version comparisons, and automated CI/CD validation to catch regressions.
Data quality monitoring
Monitors data pipelines for anomalies and schema drift to prevent downstream model failures and ensure clean, trustworthy data.
AI governance and compliance
Automates AI governance by mapping models to frameworks such as the EU AI Act and NIST, automating validation, and producing audit-ready evidence.
Real-time guardrails
Applies real-time guardrails that help prevent prompt injection, PII leakage, and hallucinations for secure agentic and LLM systems.
AI agent evaluation and observability
Tests and monitors AI and LLM agents for reliability, security, hallucinations, bias, prompt injection, planning failures, and behavioral risks across dynamic workflows.
Experiment tracking
Tracks LLM and ML experiments including prompt iterations, model settings, evaluation results, and comparisons from idea to production.
Model and data validation
Validates models for accuracy, fairness, and reliability before production and validates datasets for errors, drift, and anomalies before training or inference.
Варианты использования
- #1
Offline evaluation from prototype to production
Support ML and LLM systems with ongoing testing to safely move from prototype to production.
- #2
Production observability and monitoring
Observe and monitor AI systems in real time to catch issues in production and resolve them quickly.
- #3
Automated data quality checks
Connect data pipelines and automatically test for schema changes, drift, and anomalies before bad data reaches models.
- #4
Automated AI compliance
Align AI systems with standards such as ISO/IEC 42001, OWASP, NIST, and the EU AI Act for governance and audit readiness.
- #5
GenAI and agent testing before release
Evaluate generative AI systems and agents across edge cases, hallucinations, prompt quality, security, and behavior before user impact.
Тарифные планы
A Pricing page is linked on the official site, but no pricing tiers, plans, or costs are described in the provided source snapshots. Pricing details are not clearly published by the official source.
Альтернативы
Часто задаваемые вопросы
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