Introduction to Machine Learning Models (AI) Testing

Explore the machine learning model life cycle, covering data preparation, model engineering, and evaluation in offline development and online production modes, with emphasis on testing across phases.

Learn how machine learning models work in brief: train with data using features and labels, validate with unseen data, and manage overfitting and underfitting through hyper tuning.

Explore how underfitting, good fit, and overfitting emerge during iterative ML model training through validation tests, unseen data, and tuning for production readiness.

Understand how a machine learning model predicts house prices from features like size, bedrooms, and age, using training data, linear regression, train/test splits, and fit/predict with evaluation.

Learn how overfitting harms generalization and identify signs like high training accuracy with poor test performance. Apply regularization, cross-validation, and add more meaningful features to improve the model.

Explore unsupervised learning in machine learning, where models train on input data without labels and form clusters to segment audiences for targeted ads.

Explore how unsupervised learning groups data point patterns into clusters, uses silhouette scores, and performs white-box quality assurance checks with underfitting, overfitting, and validation data.

Review unsupervised learning by training on data clusters with training and validation sets, and evaluate with silhouette scores to detect overfitting or underfitting from a qa perspective.

Explore reinforcement learning, where models learn by interacting with their environment, receiving rewards or penalties, and improving through trial and error, alongside supervised and unsupervised learning examples.

Learn how to select algorithms for supervised and unsupervised learning, from linear and logistic regression to neural networks and clustering, using Python frameworks and testing across training, validation, and evaluation.

Enter the evaluation phase and apply qa practices to machine learning models, vs. web testing. Prepare unseen validation data, training data, testing data, and functional tests from business needs.

Explore temperature testing, which controls randomness in generative models; lower temperatures yield deterministic answers, higher temperatures enable more detailed, creative outputs, with token limits and testing for production.

Explore prompts testing techniques for machine learning models, including temperature tuning for web versus mobile, zero-shot handling, and chain-of-thought prompts with step-by-step reasoning.

Explore relevance testing and fantasy claims for ML models, validating topic sticking through temperature-based variations, real-time learning, and accuracy metrics in image classification.

Assess machine learning model reliability through repeatability testing, ensuring identical answers across repeated questions and varied phrasings, and develop diverse test cases to capture learning adaptability.

Explore style transfer testing and intent recognition testing for ml models, focusing on tone control, context management, and robust prompt memory.

Learn invariance and bidirectional testing for AI models, ensuring predictions stay stable when irrelevant data changes or inputs are swapped, and practice evaluating unseen data against core conditions.

Test end-to-end api integration for machine learning models by exploring the ui layer, the api layer, and the ChatGPT endpoint, and automate scenarios with Postman.

Download and set up Postman tool to hit ChatGPT API endpoints and observe responses. Build and run API tests for machine learning models in minutes using headers, payloads, and keys.

Learn to automate API testing in Postman using Post Bot to generate scripts, validate JSON responses, ensure content exists and is under 200 words, and implement automated test cases.

Practice responsible ai testing by assessing fairness, bias, and safety in training data and model responses to ensure ethical, transparent, and non-discriminatory outputs.

Drive competition with transparency testing and explainability by ensuring AI loan-approval decisions include clear reasons for approval or denial, aligned with financial industry needs.

Evaluate data privacy and security in AI models by testing training and validation data, guarding against personal data leakage while ensuring generalization across diverse environments.

Explore integration and latency testing for production ML models, ensuring smooth data flow, API interactions, logs, and metrics, with continuous monitoring and adherence to SLAs.

Understand data drift and concept drift in production machine learning models, and learn to test, monitor, and alert on changing input distributions and input-output relationships.

Learn how shadow testing and A/B testing certify updated ML models for production, using live data, data drift monitoring, and controlled traffic to compare performance.

QA professionals drive machine learning life cycle testing from data gathering to production and monitoring. Prioritize early involvement, domain expertise, and responsible testing including fairness, bias, and drift.