Professional Training · Applied AI Engineering Track
Machine Learning Engineering on AWS
A three-day course for machine learning professionals combining theory, labs and practical activities. It covers processing and engineering data for ML tasks with AWS services, selecting algorithms and modelling approaches for a given problem, designing scalable pipelines for training, deployment and orchestration, automating them with CI/CD, securing ML resources, and monitoring deployed models for issues such as data drift.
Authorised Instructors
Eric Tai
Head, Technology
- Machine Learning Engineer – Associate
- Security – Specialty
- DevOps Engineer – Professional
- Solutions Architect – Professional
- +4 more
Hui Liang
Chief Digital Officer
- Machine Learning Engineer – Associate
- Security – Specialty
- Generative AI Developer – Professional
- Solutions Architect – Professional
- +5 more
Any of these AWS Authorised Instructors may deliver this class, depending on the scheduled run. Trainers confirmed on enrolment.
Who Should Attend
- Machine Learning Engineers: Engineers building and operationalising models on AWS.
- Data Engineers: Engineers preparing data for ML workloads.
- DevOps Engineers: Engineers automating ML delivery pipelines.
- Developers: Developers integrating ML into production systems.
- SysOps Engineers: Operators running ML infrastructure.
Prerequisites
This is an intermediate course. To get the most from it, you should have:
Familiarity with basic machine learning concepts.
Working knowledge of Python and common data science libraries such as NumPy, pandas and scikit-learn.
A basic understanding of cloud computing concepts and familiarity with AWS.
Experience with version control systems such as Git is helpful but not required.
Learning Objectives
In this course, you will learn to:
Ground ML on AWS: Explain machine learning fundamentals and their application in the AWS Cloud.
Engineer data for ML: Process, transform and prepare data using AWS services.
Select modelling approaches: Choose algorithms based on problem requirements and interpretability.
Build scalable pipelines: Design training, deployment and orchestration pipelines on AWS.
Automate delivery: Create CI/CD pipelines for machine learning workflows.
Secure and monitor: Protect ML resources and detect issues such as data drift in production.
Certification
This course maps to the AWS Certified Machine Learning Engineer – Associate exam, the applied credential for engineers putting models into production. As an AWS Advanced Tier Authorised Training Partner, we combine hands-on pipeline work with exam-focused review.
The examination evaluates your competency across these domains:
Data Preparation for Machine Learning (ML): 28% of scored content.
ML Model Development: 26% of scored content.
Deployment and Orchestration of ML Workflows: 22% of scored content.
ML Solution Monitoring, Maintenance, and Security: 24% of scored content.
- Format
- 65 questions; multiple choice or multiple response
- Type
- Associate level certification
- Delivery Method
- Pearson VUE testing center or online proctored exam
- Time Duration
- 130 minutes
- Investment
- 150 USD
Pricing on Request
- Duration
- 3 Days
- Level
- Intermediate
- Certification
- AWS Certified Machine Learning Engineer – Associate
In-person in Kuala Lumpur or live virtual. Team & enterprise rates available.