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
Unlock AI secrets with our eBooks
Elevate your GenAI and LLM knwoledge
Navigate the core of ML observability
Metrics, feature importance and more
Welcome to the Aporia ML Observability Basics Academy! This program is designed to provide machine learning engineers, MLOps engineers, data scientists, and other ML practitioners with a comprehensive, high-level understanding of observability in the context of machine learning. Throughout this academy, we will explore various use cases, including recommender systems, dynamic pricing, NLP, and more.
In the first part of the program, we will cover the fundamentals of ML observability, including monitoring model performance, data drift, and model biases. We will then introduce you to essential machine learning tools and practices to seamlessly integrate observability into your ML pipelines.
The second part of the academy will delve into the world of MLOps. Join us as we unlock the power of observability in ML and empower you to build trustworthy and high-performing machine learning systems.
Don’t feel like you can finish all the lessons in one day? No problem, put us on your calendar for quick and easy reminder here.
You’ll need basic understanding of the following: