AI Data Engineering Training Course

AI Data Engineering Training Course

Introduction

AI Data Engineering Training Course is designed to equip professionals with advanced skills in data pipelines, artificial intelligence (AI), machine learning (ML), big data engineering, cloud data platforms, data automation, and intelligent data infrastructure. As organizations accelerate digital transformation, the ability to collect, process, transform, govern, and optimize massive datasets has become a critical capability. This course provides hands-on expertise in building AI-ready data ecosystems, implementing modern data architectures, managing real-time data processing, and applying data engineering best practices for scalable AI applications.

Participants will explore emerging technologies such as Generative AI data workflows, data lakes, data warehouses, MLOps, cloud-native engineering, automated ETL/ELT pipelines, data quality management, and AI-driven analytics. Through practical projects and industry case studies, learners will develop the ability to design reliable data platforms that support large language models (LLMs), predictive analytics, intelligent automation, and enterprise AI solutions.

Course Duration

5 days

Course Objectives

By the end of this course, participants will be able to:

1. Understand modern AI data engineering architectures and enterprise data ecosystems. 
2. Design scalable ETL/ELT pipelines for AI and analytics workloads. 
3. Implement advanced data ingestion, transformation, and orchestration workflows. 
4. Build and manage cloud-native data platforms for AI applications. 
5. Apply big data engineering techniques using distributed computing frameworks. 
6. Develop efficient data pipelines for machine learning and generative AI models. 
7. Implement data quality, validation, and observability frameworks. 
8. Manage data governance, security, compliance, and privacy standards. 
9. Work with real-time streaming data engineering technologies. 
10. Optimize data storage, performance, and scalability strategies. 
11. Apply MLOps and DataOps principles for automated AI workflows. 
12. Integrate AI automation tools into modern data engineering processes. 
13. Build production-ready AI-powered data solutions using industry best practices. 

Target Audience

1. Data Engineers 
2. AI Engineers 
3. Machine Learning Engineers 
4. Cloud Engineers 
5. Data Scientists 
6. Database Administrators 
7. Software Developers 
8. Business Intelligence and Analytics Professionals 

Course Modules

Module 1: Foundations of AI Data Engineering

• Introduction to AI-driven data ecosystems and architectures
• Understanding the role of data engineering in AI transformation 
• Data lifecycle management and modern data workflows 
• Overview of structured, semi-structured, and unstructured data 
• Designing AI-ready data platforms 
• Case Study: Building a data foundation for an e-commerce company using AI recommendation systems.

Module 2: Advanced Data Ingestion and Integration

• Designing scalable data ingestion pipelines
• Working with APIs, databases, files, and streaming sources 
• Implementing automated data collection workflows 
• Data integration patterns for enterprise environments 
• Managing high-volume data ingestion challenges 
• Case Study: Creating a real-time customer analytics pipeline for a financial services organization.

Module 3: ETL, ELT, and Data Pipeline Engineering

• Building modern ETL and ELT architectures
• Pipeline automation and workflow orchestration 
• Data transformation strategies for AI workloads 
• Pipeline monitoring and error handling 
• Optimizing data processing performance 
• Case Study: Developing an automated AI training data pipeline for a healthcare analytics platform.

Module 4: Big Data Engineering and Distributed Processing

• Introduction to distributed data processing concepts 
• Working with large-scale datasets 
• Batch processing and parallel computation techniques 
• Designing scalable big data architectures 
• Optimizing performance for enterprise workloads 
• Case Study: Processing millions of IoT sensor records for predictive maintenance.

Module 5: Cloud Data Engineering for AI

• Designing cloud-based AI data architectures 
• Cloud data storage and processing services 
• Building serverless and scalable data pipelines 
• Cloud security and data management practices 
• Multi-cloud and hybrid data strategies 
• Case Study: Migrating a traditional data warehouse into a cloud AI analytics platform.

Module 6: Real-Time Data Engineering and Streaming Analytics

• Fundamentals of real-time data processing 
• Building event-driven data architectures 
• Stream processing concepts and technologies 
• Real-time analytics and decision systems 
• Managing high-speed data environments 
• Case Study: Creating a fraud detection system using real-time transaction streams.

Module 7: Data Governance, Quality, and Security

• Implementing enterprise data governance frameworks 
• Data quality monitoring and validation techniques 
• Metadata management and data cataloging 
• Privacy protection and compliance strategies 
• Securing AI data pipelines 
• Case Study: Implementing governance controls for a regulated banking AI platform.

Module 8: AI Data Pipelines, MLOps, and Future Trends

• Preparing datasets for machine learning and LLM applications 
• Integrating DataOps and MLOps workflows 
• Automating AI model data lifecycle management 
• Building production-grade AI data infrastructure 
• Exploring future trends in autonomous data engineering 
• Case Study: Developing an enterprise Generative AI knowledge system using automated data pipelines.

Training Methodology

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.