AI Engineering Training and Certification in Doha

AI Engineering Training and Certification in Doha, Qatar

The course focuses on the concepts of machine learning, deep learning and AI system architecture.

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AI Engineering Training and Certification is a professional training program delivered by ProgNXT in Doha, Qatar. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this AI Engineering Training and Certification course with hands-on, instructor-led sessions. This training on AI Engineering provides participants with a comprehensive understanding of designing, building, and deploying AI systems. The...

Expert Panel

Designed by the ProgNXT AI & Data Science Expert Panel, specializing in Generative AI, Machine Learning, and ChatGPT applications

ProgNXT AI & Data Science Expert Panel

Course Code: PSD27
14 Hrs
Last Updated: April 2026

Course Rating5/5

This training on AI Engineering provides participants with a comprehensive understanding of designing, building, and deploying AI systems. The course focuses on the core concepts of machine learning, deep learning, and AI system architecture. Participants will gain hands-on experience with frameworks and tools used for data preprocessing, model development, and deployment in real-world applications. The training also explores best practices in scaling and managing AI solutions.

Welcome to the official AI Engineering Training and Certification certification program in Doha, Qatar. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge locally. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 14 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

Participants should have:

Basic knowledge of Python programming.
A fundamental understanding of machine learning concepts.
Familiarity with mathematics for AI, including linear algebra, probability, and statistics.
Experience with software development principles is a plus.

By the end of the training, participants will be able to:

Understand the foundational principles of AI engineering and its lifecycle.
Develop and train AI models using frameworks such as TensorFlow and PyTorch.
Perform data preprocessing and feature engineering for AI applications.
Build scalable AI pipelines for real-time and batch processing.
Integrate AI models into software systems and deploy them in production.
Apply best practices for ensuring AI system reliability and maintainability.

Upon successful completion, participants will acquire the following skills:

Building, training, and evaluating AI models.
Implementing data pipelines for AI workflows.
Deploying AI models as APIs or containerized applications.
Optimizing AI systems for scalability and performance.
Monitoring and managing deployed AI systems.

Participants will be assessed on:

Core concepts of AI Engineering: AI lifecycle, tools, and frameworks.
Model development: Data preprocessing, training, validation, and testing.
AI integration: Deploying and integrating models into real-world applications.
Scalability and optimization: Techniques for scaling AI pipelines.
Responsible AI: Ethical considerations and bias mitigation in AI systems.

Participants who complete the training and assessments will receive a ProgNXT Certificate of Completion, recognizing their expertise in AI Engineering.

Foundations and Model Development

Session 1: Introduction to AI Engineering
- Overview of the AI lifecycle.
- Tools and frameworks for AI Engineering.
- Key principles of scalable and maintainable AI systems.
Session 2: Data Preprocessing and Feature Engineering
- Techniques for data cleaning and transformation.
- Feature selection and extraction for model optimization.
Session 3: Building and Training Models
- Developing AI models using TensorFlow and PyTorch.
- Training, validation, and evaluation best practices.

Integration, Deployment, and Optimization

Session 4: AI Integration and Deployment
- Deploying AI models as APIs using Flask or FastAPI.
- Containerizing AI applications with Docker.
Session 5: Scaling AI Systems
- Real-time and batch processing pipelines using Apache Kafka and Apache Spark.
- Techniques for optimizing AI model inference.
Session 6: Responsible AI and Monitoring
- Addressing bias and ethical considerations in AI.
- Tools for monitoring and managing AI systems in production.
Hands-On Lab

This training is designed to provide participants with the skills and knowledge required to engineer scalable and reliable AI systems, equipping them to meet the demands of AI-driven industries.

Participants will receive:

Comprehensive course manuals with theoretical and practical explanations.
Access to code samples and datasets for hands-on exercises.
A curated list of online resources and tools for continued learning.
Templates and guidelines for AI pipeline development and deployment.

Participants will be evaluated through a hands-on project and a final quiz.
ProgNXT Certificates of completion will be awarded to participants who successfully complete the assessments.

AI Engineering Training and Certification Events in doha

Online Events

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More Upcoming Session Dates

November 2026

📍 Doha, Qatar

2nd Nov 2026→3rd Nov 2026

🌐 Online Instructor-Led

7th Nov 2026→8th Nov 2026

📍 Doha, Qatar

21st Nov 2026→22nd Nov 2026

🌐 Online Instructor-Led

22nd Nov 2026→23rd Nov 2026

December 2026

🌐 Online Instructor-Led

4th Dec 2026→5th Dec 2026

📍 Doha, Qatar

11th Dec 2026→12th Dec 2026

📍 Doha, Qatar

22nd Dec 2026→23rd Dec 2026

🌐 Online Instructor-Led

23rd Dec 2026→24th Dec 2026

Events for other courses in Doha, Qatar

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AI Engineering Training and Certification in Doha