Machine Learning Data Pipeline Engineering Training Course

 Machine Learning Data Pipeline Engineering Training Course 

Introduction 

Machine Learning Data Pipeline Engineering is a critical discipline for organizations seeking to build scalable, reliable, automated, and production-ready Artificial Intelligence (AI) and Machine Learning (ML) solutions. Machine Learning Data Pipeline Engineering Training Course provides comprehensive knowledge and practical skills in machine learning data pipeline architecture, data ingestion, data transformation, feature engineering, data validation, workflow orchestration, and automated model training. Participants will explore modern technologies such as Apache Airflow, Apache Spark, Python, Kafka, MLflow, Docker, Kubernetes, and cloud-based data engineering platforms to develop robust machine learning workflows. Emphasis is placed on building end-to-end data pipelines that support real-time processing, batch processing, predictive analytics, model deployment, and continuous integration and continuous delivery (CI/CD). 

The course also examines advanced machine learning pipeline automation, scalable data processing, feature stores, data versioning, model monitoring, and Machine Learning Operations (MLOps). Through practical exercises and international industry case studies, participants will learn how to overcome data quality challenges, reduce pipeline failures, improve model reproducibility, and optimize machine learning performance. The programme explores applications across financial services, healthcare, telecommunications, manufacturing, and e-commerce, highlighting global practices for developing intelligent data infrastructure. By the end of the training, participants will be equipped to design, implement, monitor, and maintain enterprise-grade machine learning data pipelines that accelerate digital transformation and data-driven decision-making. 

Course Objectives 

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

1. Understand machine learning data pipeline architecture and engineering principles.
2. Design scalable end-to-end machine learning data workflows.
3. Implement automated data ingestion and transformation processes.
4. Apply Python programming for machine learning data engineering.
5. Develop efficient batch and real-time data processing pipelines.
6. Implement advanced data cleaning, validation, and quality assurance.
7. Apply feature engineering and feature store management techniques.
8. Automate machine learning workflows using Apache Airflow.
9. Integrate MLOps practices and CI/CD automation.
10. Implement data versioning and model reproducibility.
11. Deploy scalable pipelines using cloud computing technologies.
12. Establish pipeline monitoring, observability, and performance optimization.
13. Strengthen machine learning governance, security, and operational reliability.

Organizational Benefits
 

1. Improved efficiency through automated machine learning workflows.
2. Reduced operational costs and manual data processing.
3. Enhanced data quality, consistency, and reliability.
4. Faster machine learning model development and deployment.
5. Improved scalability of enterprise data infrastructure.
6. Increased accuracy through effective feature engineering.
7. Stronger collaboration between data engineering and data science teams.
8. Improved regulatory compliance and data governance.
9. Reduced pipeline downtime through proactive monitoring.
10. Accelerated innovation and data-driven business decisions.

Target Audiences
 

1. Data Engineers and Data Architects.
2. Machine Learning Engineers.
3. Data Scientists and AI Specialists.
4. MLOps and DevOps Engineers.
5. Business Intelligence Professionals.
6. Cloud Computing and Big Data Specialists.
7. IT Managers and Technology Consultants.
8. Software Engineers and Analytics Professionals.

Course Duration: 5 days

Course Modules

Module 1: Fundamentals of Machine Learning Data Pipeline Engineering
 

• Machine learning pipeline architecture and lifecycle management.
• Data engineering principles for AI and predictive analytics.
• Batch processing versus real-time data pipelines.
• Pipeline components, dependencies, and workflow design.
• Enterprise applications of machine learning data infrastructure.
• Case study: Netflix recommendation data pipeline architecture.

Module 2: Data Ingestion and Integration Technologies
 

• Structured, semi-structured, and unstructured data ingestion.
• Building ingestion workflows using Python and SQL.
• API integration, Kafka, and streaming data collection.
• Database connectivity and cloud data integration.
• Data extraction automation and ingestion reliability.
• Case study: Uber's large-scale data ingestion and processing ecosystem.

Module 3: Data Cleaning, Transformation and Quality Engineering
 

• Data preprocessing, normalization, and standardization.
• Handling missing values, duplicates, and inconsistent datasets.
• ETL and ELT pipeline development techniques.
• Automated data validation and quality monitoring.
• Data transformation using Apache Spark and Pandas.
• Case study: Airbnb's data quality and analytics engineering practices.

Module 4: Feature Engineering and Feature Store Management
 

• Feature extraction, selection, and transformation.
• Feature pipelines for supervised and unsupervised learning.
• Feature consistency and training-serving skew prevention.
• Feature store architecture and management.
• Feature reuse, discovery, and version control.
• Case study: Uber's Michelangelo machine learning platform.

Module 5: Workflow Orchestration and Pipeline Automation
 

• Workflow orchestration concepts and scheduling.
• Building automated pipelines using Apache Airflow.
• Task dependencies, retries, and failure management.
• Integrating Spark and machine learning workflows.
• Pipeline automation and CI/CD implementation.
• Case study: Spotify's automated data and machine learning workflows.

Module 6: Machine Learning Pipeline Development and MLOps
 

• Integrating data preparation with model training.
• Model experimentation and tracking using MLflow.
• Data and model versioning for reproducibility.
• Automated model validation and testing.
• MLOps lifecycle management and deployment workflows.
• Case study: Google's TensorFlow Extended (TFX) production pipeline ecosystem.

Module 7: Cloud Deployment, Scalability and Pipeline Security
 

• Deploying pipelines on AWS, Microsoft Azure, and Google Cloud.
• Containerization using Docker and Kubernetes.
• Distributed data processing and pipeline scalability.
• Data access controls, encryption, and security management.
• Cloud resource optimization and infrastructure automation.
• Case study: Capital One's cloud-based data engineering transformation.

Module 8: Pipeline Monitoring, Observability and Optimization
 

• Pipeline monitoring and operational performance metrics.
• Data drift, model drift, and anomaly detection.
• Logging, alerting, and troubleshooting pipeline failures.
• Pipeline observability, reliability, and recovery strategies.
• Continuous improvement and production performance optimization.
• Case study: LinkedIn's monitoring and large-scale machine learning infrastructure.

Training Methodology
 

• Expert-led presentations and interactive technical discussions.
• Practical Python programming and pipeline development exercises.
• Hands-on Apache Airflow, Spark, Kafka, and MLflow demonstrations.
• Group projects involving end-to-end machine learning pipelines.
• International industry case studies and real-world problem-solving.
• Practical assessments, collaborative exercises, and knowledge-sharing sessions.

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.