Ensure reliable, on-target Gen-AI responses
Protect intellectual property and ensure compliance
Safely navigate GenAI: Detect and avoid off-topic conversations
Keep interactions tasteful, filter NSFW content
Secure company data: Detect and anonymize sensitive info
Shield data from smart LLM SQL queries
Detect and filter out malicious input for prompt integrity
Safeguard LLM: Keep model instructions confidential
Explore LLM interactions for user engagement insights
Track costs, queries, and tokens for budget control
Tailored production ML dashboards to monitor key metrics
Real-time ML monitoring to detect drifts and monitor predictions
Direct Data Connectors: Monitor and observe billions of predictions
Root Cause Analysis to gain actionable insights and explore model predictions
LLM Observability for your ML: Monitor, troubleshoot and enhance efficiency
Explainable AI to understand, ensure trust, and communicate predictions
Tailored Aporia Observe for your models: Integrate any model in minutes
Integrate Aporia to every LLM and tool in the market
Empower tabular models with Aporia
Streamline AI Act compliance with Aporia Guardrails and Observe
Unlock potential in CV & NLP models
A team of Cybersecurity, Compliance, and AI Experts that ensures Aporia users top-tier protection
Optimize LLM & GenAI apps for peak performance
Your go-to resource for Aporia insights and guides
Integrate Aporia to your LLM as a Proxy with Guardrail Policies
Integrate Aporia with Your Firewall for AI Tool Security
Easily Integrate and Monitor ML Models in Production
Define ML Observability Resources as Code with SDK
Learn about AI control from our experts
Your dictionary for AI terminology.
Step-by-step guides to master AI
Dive into our GitHub projects and examples
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Elevate your GenAI and LLM knwoledge
Navigate the core of ML observability
Metrics, feature importance and more
The process of setting up production ML environments and ensuring effective monitoring and observability is incredibly challenging in itself, before even reaching the stage of tackling other challenges relating to deploying new models.
There’s a clear consensus among most ML engineers – regardless of their industry or team size – that real-time observability is a crucial requirement for the success of ML models in production, because without it, they are oblivious to any issues that may occur.
The fact that 93% of the respondents encounter issues with their production models on a daily/weekly basis highlights just how important it is to monitor and identify issues quickly, because a high volume of production issues can have financial implications for the business.
With the increasing integration of LLM-based generative AI in enterprises, a major challenge arises: hallucinations, where AI produces inaccurate or nonsensical responses. This issue underscores the urgent need for solutions to ensure the accuracy and reliability of AI-generated content, essential for preventing reputational and financial harm.
Observability isn’t just a tool for monitoring ML production issues, it also helps companies understand the effectiveness and impact of ML models from a use - case perspective.
Setup and maintenance
Integration with existing tools
Limitations in providing value quickly
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