Data Engineering for Machine Learning Training Course

 Data Engineering for Machine Learning Training Course 

Introduction 

Data Engineering for Machine Learning Training Course equips professionals with the technical knowledge and practical skills required to design, develop, deploy, and manage scalable data infrastructure for machine learning applications. The course explores modern data engineering architectures, big data processing, data pipeline automation, cloud data platforms, data integration, data quality management, and machine learning data preparation. Participants gain practical exposure to Python, SQL, Apache Spark, Apache Airflow, Kafka, Databricks, Snowflake, and cloud-based data engineering solutions. Emphasis is placed on building reliable, scalable, and production-ready data pipelines that support artificial intelligence, predictive analytics, deep learning, and advanced machine learning workloads. 

The training further examines the integration of data engineering, Machine Learning Operations (MLOps), DataOps, and automated machine learning workflows to improve model performance and operational efficiency. Through practical exercises and global industry case studies, participants explore how leading organizations leverage modern data architectures to manage large datasets, automate feature engineering, implement real-time data processing, and support continuous machine learning model development. The course addresses emerging technologies, cloud-native data engineering, data governance, data security, and responsible artificial intelligence, enabling organizations to accelerate digital transformation and establish data-driven decision-making capabilities. 

Course Objectives 

• Develop scalable data engineering architectures for machine learning.
• Design automated end-to-end machine learning data pipelines.
• Master Python, SQL, and big data processing technologies.
• Implement Apache Spark for distributed data processing.
• Apply automated data ingestion and transformation techniques.
• Integrate cloud data platforms with machine learning workflows.
• Implement feature engineering and feature store architectures.
• Strengthen data quality, validation, and observability.
• Apply real-time streaming using Apache Kafka.
• Integrate DataOps and Machine Learning Operations (MLOps).
• Implement data governance, privacy, and security frameworks.
• Optimize machine learning datasets for model performance.
• Build reliable, production-ready machine learning infrastructure.

Organizational Benefits
 

• Improved machine learning development and deployment efficiency.
• Reduced data preparation and processing costs.
• Enhanced data quality and operational reliability.
• Faster delivery of artificial intelligence solutions.
• Improved scalability of enterprise data infrastructure.
• Better collaboration between data scientists and engineers.
• Strengthened data governance and regulatory compliance.
• Increased automation of data engineering workflows.
• Improved predictive analytics and business intelligence.
• Enhanced organizational innovation and competitiveness.

Target Audiences
 

• Data engineers and big data professionals.
• Machine learning engineers and practitioners.
• Data scientists and artificial intelligence specialists.
• Cloud architects and infrastructure engineers.
• Database administrators and data platform developers.
• Machine Learning Operations (MLOps) engineers.
• Business intelligence and analytics professionals.
• IT managers and digital transformation leaders.

Course Duration: 5 days

Course Modules

Module 1: Foundations of Data Engineering for Machine Learning
 

• Data engineering principles and machine learning architecture.
• Data lifecycle management and scalable data infrastructure.
• Structured, semi-structured, and unstructured datasets.
• Data engineering versus machine learning engineering.
• Enterprise architecture case study: Netflix data-driven recommendation systems.
• Data pipeline requirements for artificial intelligence applications.

Module 2: Python, SQL and Data Processing Technologies
 

• Python programming for machine learning data engineering.
• Advanced SQL queries and database optimization.
• Data manipulation using Pandas and NumPy.
• Distributed computing with Apache Spark.
• Global case study: Uber's large-scale data processing ecosystem.
• Practical implementation of data transformation workflows.

Module 3: Data Ingestion and Pipeline Development
 

• Batch processing and automated data ingestion.
• API integration and database connectivity.
• Data extraction, transformation, and loading (ETL).
• Workflow orchestration using Apache Airflow.
• Global case study: Airbnb's data pipeline architecture.
• Building automated and reusable data pipelines.

Module 4: Big Data, Streaming and Real-Time Processing
 

• Distributed data processing and storage architectures.
• Real-time ingestion using Apache Kafka.
• Stream processing and event-driven architectures.
• Data lake and lakehouse implementation.
• Global case study: LinkedIn's real-time data infrastructure.
• Building streaming pipelines for machine learning applications.

Module 5: Feature Engineering and Machine Learning Data Preparation
 

• Feature extraction, selection, and transformation.
• Data normalization and categorical encoding.
• Feature stores and feature versioning.
• Handling missing values and imbalanced datasets.
• Global case study: Uber's Michelangelo machine learning platform.
• Developing reusable machine learning feature pipelines.

Module 6: Cloud Data Engineering and Platform Integration
 

• Cloud-based data engineering architectures.
• Data processing with AWS, Microsoft Azure, and Google Cloud.
• Databricks and Snowflake platform integration.
• Cloud storage, data lakes, and warehouse solutions.
• Global case study: Spotify's cloud-based data analytics ecosystem.
• Deploying scalable cloud-native machine learning pipelines.

Module 7: Data Quality, Governance and MLOps Integration
 

• Automated data validation and quality monitoring.
• Data lineage, metadata management, and observability.
• Data security, privacy, and access management.
• Integrating DataOps and Machine Learning Operations (MLOps).
• Global case study: Google's production machine learning infrastructure.
• Continuous integration and continuous delivery for data pipelines.

Module 8: Production Deployment and Pipeline Optimization
 

• Production-ready machine learning data architectures.
• Pipeline performance monitoring and optimization.
• Fault tolerance, scalability, and disaster recovery.
• Automated retraining and continuous data integration.
• Global case study: Amazon's machine learning recommendation infrastructure.
• Capstone project: Developing an end-to-end machine learning data pipeline.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using industry-standard data engineering tools.
• Hands-on exercises involving Python, SQL, and Apache Spark.
• Group assignments and real-world global industry case studies.
• Capstone projects, collaborative problem-solving, and knowledge assessments.

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.