AI Testing
Master the art and science of testing AI systems. From unit testing ML pipelines to evaluating LLMs, from adversarial robustness to fairness testing — build the skills to ship reliable, trustworthy AI.
All Courses
20 comprehensive courses covering every aspect of AI and ML testing.
Foundations & Strategy
AI Model Testing Fundamentals
Master the core concepts of AI model testing including metrics, validation strategies, and building comprehensive test p...
7 LessonsUnit Testing for ML Pipelines
Learn to write robust unit tests for machine learning code using pytest, covering data transformations, feature engineer...
7 LessonsTest-Driven ML Development
Apply test-driven development principles to machine learning with data contract testing, model behavior tests, and conti...
7 LessonsAutomated ML Testing Pipelines
Build CI/CD pipelines for ML testing with GitHub Actions, automated data validation, model quality gates, and end-to-end...
7 LessonsData & Pipeline Testing
Data Validation & Testing
Learn data quality testing with Great Expectations, schema validation, statistical data tests, and automated data profil...
7 LessonsModel Performance Testing
Benchmark and profile AI models for latency, throughput, memory usage, and GPU utilization with practical optimization s...
7 LessonsAI Test Automation Frameworks
Explore and build AI test frameworks with Deepchecks, Evidently AI, MLTest library, Checklist for NLP, and custom ML tes...
7 LessonsTesting Data Pipelines
Test ETL and data pipelines with Airflow DAG testing, Spark pipeline testing, data lineage validation, and pipeline idem...
7 LessonsModel Evaluation & Quality
A/B Testing for AI Systems
Design and analyze experiments for AI systems including sample size calculation, statistical analysis, multi-armed bandi...
7 LessonsAdversarial Testing for ML
Test ML model robustness against adversarial attacks including perturbation attacks, evasion techniques, and automated a...
7 LessonsBias & Fairness Testing
Detect and measure AI bias with fairness metrics, demographic parity, equalized odds, IBM AI Fairness 360, and Google Wh...
7 LessonsRegression Testing for Models
Prevent model degradation with baseline comparisons, automated regression suites, performance threshold alerts, and vers...
7 LessonsAI Application Testing
LLM Evaluation & Testing
Evaluate and test large language models with benchmark suites, human evaluation, automated scoring, hallucination detect...
7 LessonsTesting RAG Applications
Test retrieval-augmented generation systems with retrieval quality metrics, context relevance, answer faithfulness, and ...
7 LessonsVisual AI Testing
Test computer vision models with image classification testing, object detection evaluation, segmentation metrics, and au...
7 LessonsTesting AI Chatbots
Test AI chatbot systems with intent recognition testing, dialog flow testing, response quality evaluation, and user simu...
7 LessonsInfrastructure & Operations
API Testing for AI Services
Test ML prediction APIs with request validation, load testing, error handling, contract testing, and quality monitoring ...
7 LessonsLoad Testing AI Endpoints
Master load testing for AI services with Locust, k6, stress testing GPU services, auto-scaling validation, and capacity ...
7 LessonsIntegration Testing for ML Systems
Test end-to-end ML system integration including data ingestion, feature stores, model serving, databases, and message qu...
7 LessonsMLOps Testing Strategies
Testing strategies for MLOps including model training jobs, model registry, deployment testing, canary testing, and moni...
7 LessonsWhat You'll Learn
Skills you will gain across these 20 AI testing courses.
Model Evaluation
Master metrics, cross-validation, statistical significance testing, and comprehensive model evaluation strategies for any ML system.
Test Automation
Build automated testing pipelines with pytest, CI/CD integration, quality gates, and continuous monitoring for ML systems.
Fairness & Safety
Detect bias, evaluate fairness, test adversarial robustness, and ensure your AI systems are safe and equitable for all users.
LLM & RAG Testing
Evaluate language models, detect hallucinations, test RAG applications, and build reliable AI-powered conversational systems.
AI Testing fills a gap that classical software testing does not cover well. The things that break an AI system (hallucinations, drift, bias amplification, prompt-injection, tool misuse, jailbreaks, silent regressions after a model or prompt change) are not caught by unit tests, integration tests, or even end-to-end tests in the traditional sense. They require eval design, red teaming, continuous behavioral monitoring, and the discipline to treat your prompt and model as inputs that need regression tracking.
This track covers the practical methods that teams shipping reliable AI have converged on: golden-set evaluation, LLM-as-judge (and its limits), synthetic eval generation, adversarial probing, differential testing across model versions, and production observability designed to catch behavioral drift before users do. The lessons include the tools (OpenAI evals, lm-eval-harness, Arize, Langfuse, Patronus, Ragas) and the patterns for when each is worth adopting.