Master testing for AI systems: validate machine learning models, build reliable RAG/LLM evaluation pipelines, and automate model quality checks. Hands-on labs, practical evaluation metrics, and step-by-step automation examples to make you production-ready for ML/LLM testing.

What this Bundle covers:

This bundle combines two focused courses to make you effective at testing AI models and building repeatable evaluation workflows. Start with Introduction to Machine Learning Models (AI) Testing to learn how to test model correctness, robustness, fairness, performance, and deployment behavior across common ML models. Continue with RAG-LLM Evals & Test Automation for Beginners to learn retrieval-augmented generation evaluation, automated LLM scoring, building eval harnesses, and integrating tests into CI pipelines. Each course includes hands-on examples, reusable scripts, evaluation templates, and best practices so you can apply these skills to real ML/LLM projects.

What I will Learn:

• Introduction to Machine Learning Models (AI) Testing:
  
  
• ML testing fundamentals: test types for models (unit, integration, data, drift), test strategy, and lifecycle considerations
  
  
• Data validation and preprocessing tests: schema checks, distribution drift, label quality, and data pipeline assertions
  
  
• Model correctness and performance: accuracy, precision/recall, ROC/AUC, calibration, and error analysis techniques
  
  
• Robustness and adversarial checks: noise, input perturbation, edge cases, and boundary testing
  
  
• Fairness and bias testing: group-wise metrics, fairness definitions, mitigation checks, and reporting
  
  
• Model monitoring basics: production metrics, drift detection, alerting thresholds, and rollback criteria
  
  
• Practical deliverables: test checklists, example unit tests for preprocessing and model logic, and sample monitoring dashboards
  
  
  
• RAG-LLM Evals & Test Automation for Beginners:
  
  
• RAG & LLM evaluation concepts: relevance, faithfulness, hallucination detection, and factuality metrics
  
  
• Designing evaluation datasets: prompts, reference answers, gold sets, and adversarial probes
  
  
• Automated eval tooling: using open-source eval frameworks, custom scoring functions, and human-in-the-loop setups
  
  
• E2E eval pipelines: retrieval quality tests, generator correctness checks, and combined RAG metrics
  
  
• Building CI-friendly evals: repeatable runs, seed control, metric thresholds, and automated failure gating
  
  
• Reporting and triage: result dashboards, root-cause hints, and integration with issue trackers for regressions
  
  
• Practical deliverables: sample eval scripts, scorer functions, dataset templates, and CI pipeline examples
  
  
• Practical assets and patterns:
  
  
• Ready-to-run test templates for data, model, and RAG/LLM evaluations
  
  
• Example code for unit tests, integration checks, and automated eval runners
  
  
• Metric dashboards, alerting templates, and reporting formats for stakeholders
  
  
• Guidance on human evaluation workflows and combining automated + human signals
  
  

Downloadable assets and practice material: test templates, sample eval datasets, starter repos for automated evals, CI pipeline snippets, and exercises with solutions.

Become an AI-quality engineer: validate ML models for correctness, fairness, and robustness, and build automated RAG/LLM evaluation workflows that run in CI. Practical tools, templates, and reproducible examples — everything needed to move from learning to reliable AI testing in production.