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AI Engineering & Machine Learning Systems Development Training Course

Links machine learning with software and data engineering to design, build, deploy and operate reliable AI systems, from use case definition to data pipelines.

AIMO · AI Engineering, MLOps & AI DeploymentAll Levels#2083ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Course Overview

This advanced training course provides a practical and engineering-focused framework for designing, developing, deploying, and managing artificial intelligence and machine learning systems. It bridges machine learning concepts with software engineering, data engineering, model development, deployment, and operational practices required to build reliable AI solutions for real-world organizational environments. The programme covers the complete AI engineering lifecycle, from problem definition and data preparation to model development, evaluation, deployment, monitoring, and continuous improvement. Participants will explore supervised and unsupervised learning, feature engineering, model selection, experimentation, performance optimization, and the engineering considerations required to transform machine learning models into production-ready systems. Particular emphasis is placed on scalable AI architectures, machine learning pipelines, model serving, application integration, automation, model monitoring, data and model versioning, and operational reliability. Participants will also examine responsible AI considerations, including model governance, security, explainability, data quality, privacy, and risk management. Through practical exercises, technical case studies, system design activities, and an integrated project, participants will develop the ability to design and implement end-to-end machine learning systems that can be integrated into business and operational environments.

Learning Objectives

  • By the end of the course, participants will be able to:
  • Analyze the core principles of AI engineering and machine learning systems development.
  • Define AI and machine learning use cases based on business and operational requirements.
  • Design data pipelines and prepare datasets for machine learning applications.
  • Apply appropriate machine learning algorithms and model development techniques.
  • Perform feature engineering, model selection, and performance evaluation.
  • Design scalable architectures for machine learning applications.
  • Develop reproducible machine learning workflows and experimentation processes.
  • Deploy machine learning models into production environments.
  • Implement model serving, monitoring, versioning, and lifecycle management.
  • Optimize machine learning systems for performance, scalability, and reliability.
  • Apply governance, security, explainability, and responsible AI principles.
  • Develop an end-to-end AI engineering implementation and deployment strategy.

Who Should Attend

This course is designed for AI Engineers, Machine Learning Engineers, Data Scientists, Data Engineers, Software Engineers, AI Developers, and Technology Professionals involved in developing and deploying intelligent systems. It is also suitable for IT Managers, Digital Transformation Professionals, Solution Architects, Technology Leads, Analytics Professionals, and technical project managers responsible for AI and machine learning initiatives. The programme is particularly relevant to professionals working in organizations seeking to develop production-ready AI solutions, automate intelligent processes, integrate machine learning into existing systems, and establish scalable AI capabilities.

Learning Outcomes

  • Upon completion of the course, participants will be able to:
  • Translate organizational requirements into practical AI and machine learning solutions.
  • Design end-to-end machine learning system architectures.
  • Prepare, transform, and validate data for machine learning workflows.
  • Select and develop appropriate machine learning models.
  • Apply feature engineering and model optimization techniques.
  • Evaluate model accuracy, reliability, and operational performance.
  • Build reproducible machine learning development pipelines.
  • Deploy machine learning models through suitable production architectures.
  • Implement model monitoring, version control, and lifecycle management.
  • Integrate AI models with applications, APIs, and enterprise systems.
  • Identify and manage data, model, security, and operational risks.
  • Develop a complete AI engineering roadmap from experimentation to production.

Course Outline

Course Outline

Day 1

AI Engineering Fundamentals & Machine Learning System Architecture

  • AI engineering principles and lifecycle
  • From business problem to machine learning solution
  • Machine learning system components
  • AI solution architecture
  • Data, model, application, and infrastructure layers
  • Supervised and unsupervised learning applications
  • Model development lifecycle
  • Selecting appropriate AI and machine learning approaches
  • Technical and operational requirements
  • Practical Application: Designing an End-to-End Machine Learning System Architecture
Day 2

Data Engineering, Feature Engineering & Model Development

  • Data acquisition and data preparation
  • Data quality and validation
  • Data preprocessing and transformation
  • Feature engineering and feature selection
  • Training, validation, and test datasets
  • Model selection and experimentation
  • Supervised learning algorithms
  • Unsupervised learning techniques
  • Model evaluation and performance metrics
  • Workshop: Developing a Machine Learning Pipeline from Raw Data to Model Evaluation
Day 3

Machine Learning Operations, Deployment & Model Serving

  • Machine learning development workflows
  • Reproducible experimentation
  • Model versioning and artifact management
  • Machine learning pipelines
  • Model deployment strategies
  • Batch and real-time inference
  • Model serving and application integration
  • APIs and machine learning services
  • Deployment environments and scalability
  • Practical Application: Designing and Deploying a Production-Ready Machine Learning Model
Day 4

AI System Performance, Monitoring & Reliability

  • Machine learning system monitoring
  • Model performance monitoring
  • Data drift and model drift
  • Model retraining strategies
  • Performance optimization
  • Scalability and resource management
  • Reliability and fault management
  • Automated testing for machine learning systems
  • Operational metrics and service-level considerations
  • Workshop: Developing an AI Monitoring and Continuous Improvement Framework
Day 5

Responsible AI, Governance & Advanced AI Engineering

  • Responsible AI principles
  • AI governance and model lifecycle controls
  • Model explainability and transparency
  • Data privacy and security considerations
  • Bias and fairness assessment
  • AI risk management
  • Enterprise AI integration and architecture
  • Scaling machine learning systems across the organization
  • AI engineering roadmap and implementation planning
  • Final Integrated Workshop: Designing an End-to-End AI Engineering and Machine Learning System, including architecture, data pipeline, model development, deployment, monitoring, governance, and continuous improvement.

Upcoming Dates

Selected date

Amman · 27 – 31 Dec 2026 · Classroom

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CityDatesPrice per ParticipantEarly Bird
OnlineScheduled
27 – 31 Dec 2026€2,700€2,430until 7 Dec
DubaiScheduled
27 – 31 Dec 2026€4,700€4,230until 7 Dec
AmmanScheduled
27 – 31 Dec 2026€4,000€3,600until 7 Dec
BarcelonaScheduled
28 Dec 2026 – 1 Jan 2027€6,000€5,400until 7 Dec
MadinahScheduled
28 Dec 2026 – 1 Jan 2027€4,800€4,320until 7 Dec
ViennaScheduled
28 Dec 2026 – 1 Jan 2027€6,400€5,760until 7 Dec
RomeScheduled
28 Dec 2026 – 1 Jan 2027€7,000€6,300until 7 Dec
BangkokScheduled
28 Dec 2026 – 1 Jan 2027€6,700€6,030until 7 Dec
ParisScheduled
28 Dec 2026 – 1 Jan 2027€6,700€6,030until 7 Dec
BerlinScheduled
28 Dec 2026 – 1 Jan 2027€6,300€5,670until 7 Dec
BakuScheduled
28 Dec 2026 – 1 Jan 2027€5,300€4,770until 7 Dec
MarrakeshScheduled
28 Dec 2026 – 1 Jan 2027€4,700€4,230until 7 Dec
MuscatScheduled
28 Dec 2026 – 1 Jan 2027€6,300€5,670until 7 Dec
MakkahScheduled
28 Dec 2026 – 1 Jan 2027€4,800€4,320until 7 Dec
RiyadhScheduled
28 Dec 2026 – 1 Jan 2027€4,800€4,320until 7 Dec
IstanbulScheduled
28 Dec 2026 – 1 Jan 2027€5,300€4,770until 7 Dec
FrankfurtScheduled
28 Dec 2026 – 1 Jan 2027€6,200€5,580until 7 Dec
CairoScheduled
28 Dec 2026 – 1 Jan 2027€3,800€3,420until 7 Dec
MunichScheduled
28 Dec 2026 – 1 Jan 2027€6,000€5,400until 7 Dec
CasablancaScheduled
28 Dec 2026 – 1 Jan 2027€4,700€4,230until 7 Dec
GenevaScheduled
28 Dec 2026 – 1 Jan 2027€7,300€6,570until 7 Dec
LondonScheduled
28 Dec 2026 – 1 Jan 2027€5,800€5,220until 7 Dec
AmsterdamScheduled
28 Dec 2026 – 1 Jan 2027€6,000€5,400until 7 Dec
DammamScheduled
28 Dec 2026 – 1 Jan 2027€4,800€4,320until 7 Dec
LisbonScheduled
28 Dec 2026 – 1 Jan 2027€6,000€5,400until 7 Dec
ManamaScheduled
28 Dec 2026 – 1 Jan 2027€5,300€4,770until 7 Dec
DohaScheduled
28 Dec 2026 – 1 Jan 2027€4,700€4,230until 7 Dec
Kuala LumpurScheduled
28 Dec 2026 – 1 Jan 2027€4,700€4,230until 7 Dec
MilanScheduled
28 Dec 2026 – 1 Jan 2027€7,000€6,300until 7 Dec
MadridScheduled
28 Dec 2026 – 1 Jan 2027€6,000€5,400until 7 Dec

Available in: Istanbul · Dubai · London · Riyadh · Doha · Amsterdam

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