# AI Quality Engineering Certificate Programme

https://akdenizkariyer.com/en/egitim/yapay-zeka-kalite-muhendisligi-sertifika-programi

A **60-hour** professional development programme in assessing AI **accuracy, reliability, performance and data quality**, combining software testing, analysis, quality management and automation.

### From standards to test scenarios

The curriculum explores the ISO/IEC 25059 quality model, ISO/IEC 5259 data quality principles and the ISO/IEC 42001 AI management framework, relating system development and use to quality evaluation.

### LLM and industrial applications

Examine model performance and errors, generative response quality, bias, explainability and reliability. Python test automation, MLOps, model monitoring and performance drift are included.

The computer vision module explores the quality performance of example defect detection systems. The final project aims to combine model testing, error classification and a technical quality report.

### How it differs from AI engineering

AI engineering primarily develops models and systems. AI quality engineering focuses on testing, data and output quality, risks and reliability. The two areas complement each other throughout development.

Development emphasises algorithms, model training and software implementation; quality work emphasises test automation, performance measurement, data quality, risk evaluation and identifying improvements.

Duration (minutes): 3600
Programme fee: 12,000.00 TRY
- Test AI system quality and performance
- Measure model accuracy, consistency and reliability
- Analyse error rates in machine learning models
- Develop AI-assisted software test automation
- Evaluate outputs from large language models
- Examine data quality and algorithmic bias
- Contribute to AI-assisted industrial quality control
- Prepare quality assurance and risk evaluation reports
- Provide technical support for ISO/IEC 42001 management activities
- Develop AI quality and testing services within your professional competence

Audience: Computer, software, industrial, electrical/electronic and mechatronics engineers; AI and data science practitioners; developers and software testers; quality assurance and control staff; manufacturing and automation professionals; factory managers; IT, data and business analysts; quality management staff; technology entrepreneurs; digital transformation practitioners; technical students; and other adults interested in AI quality.
- Introduction to AI and Quality Engineering (240 min)
The relationship between model development and quality evaluation.
- ISO/IEC 25059 AI Quality Model and Standards (240 min)
A model framework for evaluating the quality characteristics of AI systems.
- Data Quality, Validation and ISO/IEC 5259 (360 min)
Input data quality, validation and data quality principles.
- Quality and Performance Testing for Machine Learning Models (360 min)
Measuring accuracy, error rates and model performance.
- Quality Analysis of Large Language Models and Generative AI (360 min)
Consistency, correctness and reliability of generative model responses.
- AI Test Automation with Python (360 min)
Python applications for repeatable test scenarios.
- Computer Vision for Industrial Quality Control (300 min)
Evaluating defect detection applications using camera images.
- Testing Bias, Explainability and Reliability (300 min)
Examining bias, explainability and reliability in model behaviour.
- MLOps, Model Monitoring and Performance Drift Analysis (300 min)
Monitoring deployed model performance and identifying drift.
- ISO/IEC 42001 AI Management and Risk Assessment (300 min)
Technical contributions to AI management processes and risk assessment.
- Quality Metrics, Dashboards and Technical Reporting (240 min)
Presenting quality results through metrics, dashboards and reports.
- Practical AI Quality Engineering Final Project (240 min)
Model testing, error classification and a quality evaluation report.
Certificate eligibility depends on programme completion and assessment requirements.

## Career opportunities
### Potential work areas

- **AI Quality Engineer Within Relevant Qualifications**: Perform technical quality testing and evaluation within existing qualifications.
- **AI Quality Assurance Specialist**: Contribute to AI quality assurance.
- **AI Testing Specialist**: Develop model test scenarios.
- **Machine Learning Quality Analyst**: Analyse model accuracy and performance.
- **LLM Evaluation Specialist**: Evaluate generative AI response quality and reliability.
- **AI Risk and Compliance Support Specialist**: Support AI risk and compliance activities.
- **AI Test Automation Specialist**: Automate software and AI testing.
- **Industrial AI Quality Control Specialist**: Evaluate production-line AI quality control systems.
- **Data Quality Specialist**: Assess data used by AI models.
- **AI Quality Consultant**: Provide AI quality services within your competence.

### Institutions and projects

AI and software businesses, technology parks, automotive and defence manufacturing, banking and financial technology, e-commerce and digital platforms, telecommunications, industrial automation and robotics, smart manufacturing, data/AI consultancies, international technology firms, and independent testing and quality consultancies.

Roles and titles depend on existing education, experience, technical competence and the relevant organisation’s requirements. This is professional development training; employment and a particular income are not guaranteed.

### Example project services

- Model accuracy, reliability and error analysis
- Technical support for AI management processes
- Automated tests and scenarios
- Generative AI quality and reliability evaluation
- Evaluation of industrial vision and defect detection systems

### Example income target for advanced expertise

**TRY 200,000 per month** is an illustrative advanced-expertise target, not a market salary average or a post-course earning promise. Income depends on experience, sector, scope, working arrangements and continuity of work. Consulting revenue is different from net earnings.

## Certificates and fee
Participants who successfully complete the programme receive an **international certificate**. Acceptance depends on the requirements of the receiving organisation. Training and the certificate are included in the programme fee.

### How long is the programme?
The programme totals 60 hours across 12 curriculum modules.

### What will I create by the end?
The final project aims to combine model testing, error classification and a technical quality report.

### Who can join?
Computer, software, industrial, electrical/electronic and mechatronics engineers; AI and data science practitioners; developers and software testers; quality assurance and control staff; manufacturing and automation professionals; factory managers; IT, data and business analysts; quality management staff; technology entrepreneurs; digital transformation practitioners; technical students; and other adults interested in AI quality.

### Is a software or engineering degree required?
Non-engineering participants may join. Python, basic statistics, algorithms and software knowledge are useful for practical work.

### Which skills does it develop?
Key outcomes include test AI system quality and performance; measure model accuracy, consistency and reliability; analyse error rates in machine learning models.

### Is there a final project?
Yes. The curriculum includes a practical final project and evaluation.

### What certificate is provided?
Participants who successfully complete the programme receive an **international certificate**. Acceptance depends on the requirements of the receiving organisation. Training and the certificate are included in the programme fee.

### How are tool accounts and API use arranged?
Required accounts and any subscription or API usage conditions are clarified before enrolment.

### Are jobs or specific earnings guaranteed?
No. This is professional development. Roles depend on existing education, experience, technical competence and organisational requirements.

### Which ISO/IEC topics are covered?
ISO/IEC 25059 AI quality, ISO/IEC 5259 data quality and ISO/IEC 42001 AI management are curriculum topics. Organisational ISO certification is a separate process.

### Does it include Python and industrial quality control?
Yes. Modules cover Python test automation, evaluation of visual defect detection and model monitoring.

### Is TRY 200,000 a monthly earnings promise?
No. It is an illustrative advanced-expertise target, not a salary average or a post-course guarantee. Earnings depend on experience, scope and working conditions.

Programme information: https://akdenizkariyer.com/en/iletisim