Data Engineering for Machine Learning Course: Designing AI-Driven Data Pipelines

Introduction

The growth of artificial intelligence and machine learning has highlighted the critical role of data engineering in powering successful AI systems. Without efficient data pipelines, even the most advanced machine learning models fail to deliver meaningful results. This course provides a practical and strategic foundation for professionals aiming to design AI-driven data pipelines that ensure models have access to clean, reliable, and scalable data.

Participants will explore the complete lifecycle of machine learning data workflows, from data ingestion and transformation to feature engineering and deployment. The course introduces key frameworks such as Apache Spark, Apache Kafka, TensorFlow Extended (TFX), and cloud-native services, emphasizing real-world applications in industries such as healthcare, finance, and e-commerce.

A strong focus will be placed on hands-on practice, enabling learners to build, optimize, and monitor data pipelines that can handle both batch and real-time workloads. Through labs, case studies, and interactive sessions, participants will gain experience with challenges like data drift, pipeline orchestration, and AI governance.

This program is designed not only to enhance technical expertise but also to prepare learners for interdisciplinary collaboration between data scientists, machine learning engineers, and IT teams. By understanding the intersection between data engineering and AI, participants will be able to build systems that support enterprise-scale machine learning initiatives.

By the end of this course, learners will have the ability to design end-to-end machine learning pipelines, optimize performance, ensure compliance, and align AI initiatives with business goals, ultimately driving innovation and competitive advantage.

Who Should Attend

• Data engineers interested in specializing in AI-driven workflows.
• Machine learning engineers aiming to strengthen data pipeline skills.
• Data scientists who need scalable and production-ready data systems.
• Software developers building AI-enabled applications.
• Cloud engineers working with machine learning infrastructure.
• Database administrators transitioning to ML-ready environments.
• IT managers overseeing AI adoption and enterprise ML deployments.
• Consultants advising on AI transformation projects.
• Analytics professionals enhancing AI and ML capabilities.
• Business leaders seeking insights into AI-driven data systems.

Duration

5 days

Course Objectives

By completing this course, participants will be able to:

• Understand the role of data engineering in machine learning and AI.
• Design scalable and efficient data pipelines for ML workflows.
• Ingest and transform structured, semi-structured, and unstructured data.
• Apply feature engineering techniques to optimize ML models.
• Build pipelines with Spark, Kafka, and TFX for AI applications.
• Ensure data quality, consistency, and governance in ML workflows.
• Deploy ML pipelines on cloud-native and hybrid environments.
• Implement monitoring, versioning, and retraining strategies.
• Optimize pipeline performance for cost and scalability.
• Apply best practices through real-world AI use cases.

Comprehensive Course Outline

Module 1: Introduction to Data Engineering for ML

• Importance of data pipelines in AI and ML.
• Batch vs. streaming pipelines in ML workflows.
• The role of data engineers in AI-driven ecosystems.
• Industry applications and success stories.

Module 2: Data Ingestion and Integration

• Collecting data from APIs, databases, and streaming sources.
• Real-time ingestion with Apache Kafka and AWS Kinesis.
• Batch ingestion using ETL/ELT workflows.
• Integrating multiple data sources for ML readiness.

Module 3: Data Cleaning and Transformation

• Handling missing, inconsistent, and noisy data.
• Data normalization and transformation for ML models.
• Automating cleaning processes with pipelines.
• Building reproducible preprocessing workflows.

Module 4: Feature Engineering for Machine Learning

• Feature extraction and transformation techniques.
• Designing feature stores for production pipelines.
• Automating feature selection and optimization.
• Real-time vs. batch feature engineering strategies.

Module 5: Scalable Data Processing Frameworks

• Distributed processing with Apache Spark for ML.
• Streaming frameworks: Spark Streaming and Flink.
• Cloud-based processing with Dataflow and Dataproc.
• Balancing performance, scalability, and costs.

Module 6: ML Pipeline Orchestration

• Workflow automation with Airflow and Kubeflow.
• Introduction to TensorFlow Extended (TFX).
• CI/CD practices for machine learning pipelines.
• Automating training, testing, and deployment.

Module 7: Data Governance and Security

• Ensuring data quality, lineage, and versioning.
• Role-based security and encryption in ML workflows.
• Regulatory compliance: GDPR, HIPAA, and AI ethics.
• Monitoring and auditing ML pipelines.

Module 8: Real-Time ML Pipelines

• Building real-time ML-enabled applications.
• Streaming pipelines for recommendation and fraud detection.
• IoT integration for ML models.
• Combining real-time and batch workloads.

Module 9: Monitoring and Maintenance

• Tracking pipeline health and data drift.
• Monitoring ML model performance in production.
• Retraining workflows and lifecycle management.
• Logging and observability for ML pipelines.

Module 10: Future Trends in Data Engineering for ML

• AI-driven automation in data engineering.
• The rise of DataOps and MLOps frameworks.
• Serverless pipelines and next-generation tools.
• Generative AI integration with ML workflows.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue

The training will be held at our Upskill Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment

Unless otherwise agreed between the two parties payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better