01
Learning Objectives
- Design and develop AI agents and integrated ML applications
- Apply advanced MLOps practices for production model management
- Ensure compliance with AI governance regulations and best practices
FINAL EXAM ONLY
AI_ML_AGENTS_LA
Artificial Intelligence and Machine Learning – Building AI Agents and ML Applications (MLOps & Governance)
The course "Artificial Intelligence and Machine Learning – Building AI Agents and ML Applications (MLOps & Governance)" provides comprehensive training for designing, developing and managing AI and machine learning solutions with an advanced focus on intelligent agents. Participants will gain technical skills in using cutting-edge frameworks and technologies for fine-tuning large language models (LLMs), integrating ML pipelines, and implementing complex agentic systems. The curriculum covers best MLOps practices including data preparation, validation, deployment, monitoring with drift management, and retraining. Critical topics such as security, explainability, fairness and compliance with international standards like ISO/IEC 42001, 23894, and the EU AI Act regulation are deeply explored. Practical exercises and case studies, such as supply chain optimization using LLMs and Python systems, enable hands-on application of techniques ensuring performance and responsible governance. The course prepares professionals to build AI systems ready for production environments, addressing challenges of scalability, reliability, and compliance with privacy and security regulations.
Exam duration60 min
Pass mark70%
Available questions200
CIWZ PROFESSIONAL ASSESSMENT
High-integrity learning environment
The pathway is designed for managers, executives, function owners, auditors and professionals. Lessons use validated minimum study time and a contextual tutor; assessments use server-side random selection of questions and answers, session-continuity controls, camera and local audio-activity monitoring during protected tests.
Traceability and reliability: integrity events are logged for authorized review; automated signals do not determine the result on their own.
OFFICIAL PATHWAY INFORMATION
Course technical sheet
Purpose, references, delivery method, commercial terms and certificate value, presented clearly and transparently.
02
Skills Acquired
- Fine-tuning and deploying LLMs
- Implementing ML pipelines and agent orchestration
- Model monitoring, drift handling, retraining
- Security management, explainability, and fairness
- Understanding ISO/IEC 42001, 23894, 22989, 23053, AI Act, GDPR
03
Target Audience
- Data Scientists, ML Engineers, AI Developers
- IT professionals interested in advanced AI systems
- Researchers and managers responsible for AI projects
04
Prerequisites
- Basic knowledge of machine learning and Python programming
- Familiarity with language models and ML pipelines concepts
05
Program
- Introduction to AI agents and advanced ML
- Architectures and technologies for ML agents
- Fine-tuning and evaluation of LLMs
- MLOps: deployment, monitoring, retraining
- Security, bias, explainability, compliance
- Case studies on supply chain, medical assistants, KPI reporting
06
Teaching Methodology
- Theoretical lessons and seminars
- Practical exercises and case study analysis
- Discussions on regulations and best practices
07
Evaluation Methods
- Theoretical tests and practical case studies
- Passing threshold at 70% with formative assessments
08
Duration
60 minutes
09
Certification
Completion certificate available for additional fee
10
Expected Outcomes
- Ability to develop and manage AI agents in production
- Skills in model lifecycle management and regulatory compliance
- Critical analysis and responsible technical decision-making