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.

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20 Courses

140 Lessons

100% Free

700+ Code Examples

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...

Unit Testing for ML Pipelines

Learn to write robust unit tests for machine learning code using pytest, covering data transformations, feature engineer...

Test-Driven ML Development

Apply test-driven development principles to machine learning with data contract testing, model behavior tests, and conti...

Automated ML Testing Pipelines

Build CI/CD pipelines for ML testing with GitHub Actions, automated data validation, model quality gates, and end-to-end...

Data & Pipeline Testing

Data Validation & Testing

Learn data quality testing with Great Expectations, schema validation, statistical data tests, and automated data profil...

Model Performance Testing

Benchmark and profile AI models for latency, throughput, memory usage, and GPU utilization with practical optimization s...

AI Test Automation Frameworks

Explore and build AI test frameworks with Deepchecks, Evidently AI, MLTest library, Checklist for NLP, and custom ML tes...

Testing Data Pipelines

Test ETL and data pipelines with Airflow DAG testing, Spark pipeline testing, data lineage validation, and pipeline idem...

Model Evaluation & Quality

A/B Testing for AI Systems

Design and analyze experiments for AI systems including sample size calculation, statistical analysis, multi-armed bandi...

Adversarial Testing for ML

Test ML model robustness against adversarial attacks including perturbation attacks, evasion techniques, and automated a...

Bias & Fairness Testing

Detect and measure AI bias with fairness metrics, demographic parity, equalized odds, IBM AI Fairness 360, and Google Wh...

Regression Testing for Models

Prevent model degradation with baseline comparisons, automated regression suites, performance threshold alerts, and vers...

AI Application Testing

LLM Evaluation & Testing

Evaluate and test large language models with benchmark suites, human evaluation, automated scoring, hallucination detect...

Testing RAG Applications

Test retrieval-augmented generation systems with retrieval quality metrics, context relevance, answer faithfulness, and ...

Visual AI Testing

Test computer vision models with image classification testing, object detection evaluation, segmentation metrics, and au...

Testing AI Chatbots

Test AI chatbot systems with intent recognition testing, dialog flow testing, response quality evaluation, and user simu...

Infrastructure & Operations

API Testing for AI Services

Test ML prediction APIs with request validation, load testing, error handling, contract testing, and quality monitoring ...

Load Testing AI Endpoints

Master load testing for AI services with Locust, k6, stress testing GPU services, auto-scaling validation, and capacity ...

Integration Testing for ML Systems

Test end-to-end ML system integration including data ingestion, feature stores, model serving, databases, and message qu...

MLOps Testing Strategies

Testing strategies for MLOps including model training jobs, model registry, deployment testing, canary testing, and moni...

What 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.