Machine Learning Data Engineering on AWS Training Course

 Machine Learning Data Engineering on AWS Training Course 

Introduction 

Machine Learning Data Engineering on AWS Training Course provides comprehensive practical knowledge and technical skills for designing, developing, deploying, and managing scalable machine learning data pipelines using Amazon Web Services (AWS). The course explores modern data engineering architectures, cloud-native data processing, machine learning workflows, data ingestion, transformation, feature engineering, and automated model training. Participants gain practical experience with Amazon S3, AWS Glue, Amazon SageMaker, Amazon Redshift, Amazon Kinesis, AWS Lambda, and other essential cloud technologies used to build reliable, secure, and high-performance machine learning infrastructure. 

The training focuses on integrating machine learning with enterprise data engineering through automated ETL processes, distributed computing, real-time data streaming, data lakes, feature stores, and MLOps practices. Participants explore practical approaches to data quality management, model-ready dataset preparation, workflow orchestration, infrastructure automation, and machine learning deployment. Global case studies from financial services, healthcare, e-commerce, telecommunications, and logistics demonstrate how organizations leverage AWS machine learning data engineering to accelerate digital transformation, improve predictive analytics, optimize business intelligence, and achieve scalable AI-driven innovation. 

Course Objectives 

• Develop scalable machine learning data engineering architectures on AWS.
• Master cloud-based data ingestion and ETL pipeline development.
• Implement automated data transformation and feature engineering.
• Configure Amazon SageMaker for machine learning workflows.
• Build real-time data streaming using Amazon Kinesis.
• Implement data lake architectures using Amazon S3.
• Apply distributed processing using AWS Glue and Amazon EMR.
• Integrate machine learning datasets with Amazon Redshift.
• Implement data quality, governance, and security frameworks.
• Develop automated machine learning pipeline orchestration.
• Apply MLOps, CI/CD, and machine learning automation.
• Optimize cloud infrastructure performance and operational costs.
• Deploy reliable, scalable, production-ready machine learning data solutions.

Organizational Benefits
 

• Accelerated enterprise AI and machine learning adoption.
• Improved data processing efficiency and scalability.
• Reduced operational costs through cloud automation.
• Enhanced predictive analytics and business intelligence.
• Improved enterprise data quality and reliability.
• Faster machine learning model development cycles.
• Strengthened cloud security and data governance.
• Improved real-time business decision-making.
• Increased organizational innovation and competitiveness.
• Enhanced digital transformation capabilities.

Target Audiences
 

1. Data engineers and cloud data architects.
2. Machine learning engineers and AI developers.
3. AWS cloud architects and administrators.
4. Data scientists and analytics professionals.
5. Database administrators and data platform specialists.
6. MLOps engineers and DevOps professionals.
7. Business intelligence and big data specialists.
8. IT managers and enterprise technology consultants.

Course Duration: 5 days

Course Modules

Module 1: Foundations of Machine Learning Data Engineering on AWS
 

• Machine learning data engineering concepts and architectures.
• AWS cloud infrastructure and core services.
• Data engineering lifecycle and machine learning integration.
• Cloud-native data pipeline design and implementation.
• Enterprise case study: Netflix data-driven recommendation infrastructure.
• Practical AWS architecture development.

Module 2: AWS Data Ingestion and ETL Pipeline Development
 

• Data ingestion using Amazon S3 and AWS Glue.
• Batch processing and automated ETL workflows.
• Data transformation using AWS Glue DataBrew.
• Data integration from structured and unstructured sources.
• Case study: Amazon retail data integration and processing.
• Building automated cloud data ingestion pipelines.

Module 3: Data Lakes and Distributed Processing
 

• Data lake architecture using Amazon S3.
• AWS Lake Formation and centralized data governance.
• Distributed processing using Amazon EMR.
• Apache Spark for large-scale data transformation.
• Case study: Healthcare data lake implementation for analytics.
• Developing scalable data lake processing workflows.

Module 4: Real-Time Data Streaming and Processing
 

• Real-time ingestion using Amazon Kinesis.
• Event-driven processing using AWS Lambda.
• Stream transformation and data enrichment.
• Managing streaming workloads and processing latency.
• Case study: Uber-style real-time mobility data analytics.
• Developing real-time machine learning data pipelines.

Module 5: Machine Learning Data Preparation and Feature Engineering
 

• Data cleaning, validation, and preprocessing.
• Feature extraction and feature selection techniques.
• Amazon SageMaker Data Wrangler.
• Feature storage using Amazon SageMaker Feature Store.
• Case study: Financial fraud detection using engineered features.
• Preparing production-ready machine learning datasets.

Module 6: Machine Learning Pipelines with Amazon SageMaker
 

• Amazon SageMaker architecture and capabilities.
• Automated model training and data preparation.
• SageMaker Pipelines and workflow orchestration.
• Model evaluation, experimentation, and validation.
• Case study: Healthcare predictive analytics using AWS machine learning.
• Building automated machine learning workflows.

Module 7: MLOps, Automation, and Model Deployment
 

• MLOps principles and machine learning lifecycle management.
• CI/CD pipelines for machine learning applications.
• Model deployment using SageMaker endpoints.
• Monitoring model performance and data drift.
• Case study: E-commerce recommendation model deployment.
• Implementing automated machine learning operations.

Module 8: Data Security, Governance, and Performance Optimization
 

• AWS Identity and Access Management and encryption.
• Data governance, cataloguing, and access controls.
• Monitoring using Amazon CloudWatch.
• Cost optimization and infrastructure performance management.
• Case study: Banking data security and machine learning governance.
• Designing secure, scalable enterprise architectures.

Training Methodology
 

• Instructor-led presentations and technical demonstrations.
• Practical AWS cloud laboratory exercises.
• Hands-on machine learning data pipeline development.
• Real-world enterprise case studies and group discussions.
• Guided projects involving AWS data engineering services.
• Practical assessments, troubleshooting, and knowledge evaluation.

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.