Quality Management
Quality Assurance for AI and Machine Learning Systems
This in-depth course develops directly applicable capability in Quality Assurance for AI and Machine Learning Systems. It connects AI Quality Attributes and Requirements, Data and Pipeline Quality, and Model and System Verification to the decisions, controls, and activities participants need to perform in their workplace.
Overview
Practical learning for workplace transfer.
This in-depth course develops directly applicable capability in Quality Assurance for AI and Machine Learning Systems. It connects AI Quality Attributes and Requirements, Data and Pipeline Quality, and Model and System Verification to the decisions, controls, and activities participants need to perform in their workplace. The five-module curriculum progresses toward AI Quality Review, using evidence, scenarios, and work products appropriate to the subject.
Objectives
- Analyze ai quality attributes and requirements, including accuracy, robustness, safety, fairness, explainability, latency, and cost.
- Configure or structure data and pipeline quality, including completeness, validity, representativeness, labeling, lineage, and leakage.
- Evaluate model and system verification, including baseline comparison, subgroup performance, edge cases, stress, and adversarial tests.
- Manage release and production quality, including version, approval, deployment, monitoring, incident, and rollback.
- Apply ai quality review, including build a quality plan for an ai use case.
Target audience
- Professionals responsible for this subject area
- Managers, supervisors, and team leaders
- Analysts, specialists, engineers, or coordinators working with the relevant processes
- Project, implementation, assurance, or improvement team members
- Professionals preparing for broader responsibilities in this field
Program outline
A clear structure for the learning journey.
Program outline
Outline points are grouped in one designed block instead of being treated as separate module cards.
Module 1: AI Quality Attributes and Requirements
Accuracy, robustness, safety, fairness, explainability, latency, and cost
Define intended use, user, environment, and unacceptable failure
Translate attributes into testable acceptance criteria
Module 2: Data and Pipeline Quality
Completeness, validity, representativeness, labeling, lineage, and leakage
Training, validation, test, and production separation
Schema, drift, and transformation controls
Module 3: Model and System Verification
Baseline comparison, subgroup performance, edge cases, stress, and adversarial tests
Integration, tool, retrieval, and human-interface testing
Reproducibility and independent review
Module 4: Release and Production Quality
Version, approval, deployment, monitoring, incident, and rollback
Complaint and override analysis
Regression suites and change impact assessment
Module 5: AI Quality Review
Build a quality plan for an AI use case
Evaluate test evidence and unresolved defects
Make a release decision with conditions
Materials provided
- ○ Course-specific presentation slides
- ○ Guided exercises, scenarios, or configured-environment activities appropriate to the subject
- ○ Course-specific worksheets, checklists, or calculation templates
- ○ Applied workplace case materials
- ○ 4D Certificate of Completion issued by 4D Training & Consultancy
- ○ Post-course support for implementation questions
Training Options
Programs can be delivered in-house, online, or in a blended format depending on your team's schedule, location, and learning objectives. When an external certificate or exam is included, certification rules and fees remain under the relevant awarding body's policies, while 4D provides the training and preparation support.
Why choose 4D
4D Training & Consultancy adapts the program to the client’s operating environment. Delivery combines structured explanation with subject-specific analysis, exercises, and implementation decisions so participants can transfer the learning to real responsibilities without implying vendor authorization.
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