AI Testing and Quality Assurance Training Course

AI Testing and Quality Assurance Training Course

Introduction

AI Testing and Quality Assurance Training Course is designed to equip professionals with advanced skills in Artificial Intelligence (AI) validation, machine learning (ML) testing, intelligent automation, software quality engineering, and AI-driven testing frameworks. As organizations rapidly adopt Generative AI, Large Language Models (LLMs), AI-powered applications, and autonomous systems, the demand for experts who can ensure accuracy, reliability, security, fairness, scalability, and performance of AI solutions is increasing. This course provides practical knowledge in AI model testing, data quality assessment, algorithm validation, prompt testing, AI observability, automation testing, and continuous quality engineering.

Participants will explore modern approaches to AI Quality Assurance (AI-QA), MLOps testing, responsible AI validation, automated test generation, AI security testing, and model performance monitoring. Through hands-on labs, real-world case studies, and industry-based projects, learners will gain the ability to design comprehensive testing strategies for AI applications across industries including healthcare, finance, cybersecurity, manufacturing, retail, and enterprise software development.

Course Duration

5 days

Course Objectives

1. Understand AI Testing Frameworks and Quality Engineering principles for modern AI systems. 
2. Develop expertise in Machine Learning Model Testing and Validation techniques. 
3. Apply Generative AI Testing strategies for LLMs and AI-powered applications. 
4. Master AI Test Automation using intelligent testing tools and frameworks. 
5. Perform Data Quality Testing and Data Validation for AI pipelines. 
6. Implement MLOps Testing and Continuous AI Quality Assurance practices. 
7. Evaluate AI Model Accuracy, Reliability, Bias, and Fairness metrics. 
8. Design AI Security Testing approaches against adversarial attacks and vulnerabilities. 
9. Apply Prompt Engineering Testing and LLM Response Evaluation techniques. 
10. Build skills in AI Observability, Monitoring, and Performance Optimization. 
11. Create scalable Automated Testing Pipelines for AI Applications. 
12. Understand Responsible AI, Governance, Compliance, and Ethical Testing practices. 
13. Develop industry-ready expertise in AI Quality Engineering and Intelligent Automation. 

Target Audience

1. Software Quality Assurance Engineers 
2. AI Engineers and Machine Learning Engineers 
3. Data Scientists and Data Analysts 
4. Automation Test Engineers 
5. Software Developers building AI applications 
6. MLOps and DevOps Professionals 
7. IT Managers and Technology Leaders 
8. Product Owners and Digital Transformation Teams 

Course Modules

Module 1: Foundations of AI Testing and Quality Assurance

• Introduction to AI Quality Engineering and Testing Lifecycle
• Differences between traditional software testing and AI testing 
• AI system components: data, models, algorithms, and interfaces 
• Quality challenges in machine learning applications 
• AI testing strategies and industry best practices 
• Case Study: Testing an AI-powered customer service chatbot to improve response accuracy and reliability.

Module 2: Machine Learning Model Testing and Validation

• Model accuracy, precision, recall, and performance evaluation 
• Functional and non-functional ML model testing 
• Regression testing for machine learning models 
• Model validation and benchmarking techniques 
• Testing classification, regression, and recommendation systems 
• Case Study: Validating a healthcare prediction model to ensure accurate patient risk assessment.

Module 3: Generative AI and Large Language Model Testing

• Testing Generative AI applications and LLM-based solutions 
• Prompt testing and response quality evaluation 
• Detecting hallucinations and inaccurate AI outputs 
• LLM performance benchmarking techniques 
• Testing AI agents and conversational systems 
• Case Study: Quality testing of an enterprise AI assistant using automated evaluation metrics.

Module 4: AI Test Automation and Intelligent Testing Frameworks

• AI-powered test automation concepts 
• Automated test case generation using AI 
• AI testing tools and frameworks 
• Building continuous testing pipelines 
• Integrating AI testing into DevOps workflows 
• Case Study: Implementing automated regression testing for an AI-driven banking application.

Module 5: Data Quality Testing for AI Systems

• Data validation and data integrity testing 
• Detecting missing, inconsistent, and biased datasets 
• Feature engineering quality checks 
• Data pipeline testing in ML workflows 
• Data governance and quality monitoring 
• Case Study: Testing training datasets for a fraud detection machine learning system.

Module 6: AI Security, Bias, and Responsible AI Testing

• AI security testing methodologies 
• Adversarial testing and model robustness evaluation 
• Bias detection and fairness assessment 
• Privacy testing for AI systems 
• Responsible AI governance practices 
• Case Study: Evaluating fairness and security risks in an AI recruitment platform.

Module 7: MLOps Testing, Monitoring, and AI Observability

• Continuous testing in MLOps environments 
• AI model deployment validation 
• Model drift detection and monitoring 
• Performance tracking and reliability testing 
• AI lifecycle management practices 
• Case Study: Monitoring an AI recommendation engine after production deployment.

Module 8: Advanced AI Quality Engineering Projects

• Designing enterprise AI testing strategies 
• End-to-end AI application testing 
• AI testing metrics and reporting dashboards 
• Industry quality assurance standards 
• Capstone AI testing implementation project 
• Case Study: Building a complete AI QA framework for an intelligent business application.

Training Methodology

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.