• Home
  • AWS Certified Machine Learning Engineer Associate Training

AWS Certified Machine Learning Engineer Associate Training

Course Information

  • Sessions 3 days
  • Duration 22.5 hrs
  • Level Intermediate
  • Assessment NA

Venue

Kuala Lumpur: G-3A-02, Suite Pejabat Korporat, KL Gateway, No 2, Jalan kerinchi, Gerbang kernichi Lestari, 59200 Kuala Lumpur, Malaysia
Penang: Jalan Sungai Dua, 11700 Penang, Malaysia.

Course Brochure

Download Course Brochure

Certification

  • Certificate of Achievement from Tertiary Infotech Academy Pte Ltd - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Achievement from Tertiary Infotech Academy Pte Ltd.

AWS Certified Machine Learning Engineer Associate Training

Course Code: C1330
  • HRDF

9 Review(s) | Add Your Review

Share

What's This Course About

This AWS Certified Machine Learning Engineer Associate Exam Prep course equips you with the skills and knowledge required to excel in machine learning using AWS cloud services. You'll learn key concepts such as data engineering, exploratory data analysis, model training and deployment, and performance optimization on AWS. The course covers AWS tools like SageMaker, Lambda, and other critical AI and ML services to prepare you thoroughly for the certification exam.

Participants will gain hands-on experience with real-world projects that simulate practical applications of machine learning on AWS. This course is ideal for IT professionals, data scientists, and developers seeking to enhance their machine learning capabilities and advance their careers in AI and cloud computing.

Bonus: Free Practice Exams

Get exam-ready on our Practice Exam Portal — train in realistic Practice Mode and timed Exam Mode, then retake them as many times as you like before the real exam.

Start Practising →

WSQ Funding

Full Fee 4,200.00 Before GST
GST 378.00 9% of fee
Baseline Nett 2,478.00 SG/PR age 21+ · 50% funded
MCES / SME Nett 1,638.00 SG age 40+ · 70% funded
Funding and Grant Applications

No funding is available for this course

For WSQ funding, please checkout the details at WSQ - AWS Certified Machine Learning Engineer Associate Training

Course Fee

MYR4,200.00

Course Date

Course Time

Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

Disclaimer: The course dates displayed on our website are tentative and subject to trainer availability. We will confirm the final date after checking with the trainer. You are also welcome to email us your preferred date at sales@tertiarycourses.com.my, and we will do our best to coordinate with the trainer's schedule.

Post-Course Support

  • We may provide consultation related to the subject matter after the course.
  • Please email your queries to sales@tertiarycourses.com.my and we will forward your queries to the subject matter experts and get back to you as soon as possible.

Cancellation & Reschedule Policy

  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
  • Note: the venue of the training is subject to changes due to class size and availability of the classroom. The minimum class size to start a class is 3 Pax.

Course Details

Course Details

What You'll Learn

This course prepares you for the MLA-C01 certification exam, covering all official exam domains and their approximate weightings:

Domain 1 Domain 1: Data Preparation for Machine Learning (ML) (28%)

  • Ingest and store data from AWS sources (S3, EFS, FSx) and streaming sources (Kinesis, Apache Flink/Kafka); choose data formats (Parquet, JSON, CSV, ORC, Avro, RecordIO)
  • Transform data and perform feature engineering (cleaning, encoding, scaling/normalization, binning) using SageMaker Data Wrangler, AWS Glue/Glue DataBrew, Spark on EMR
  • Create and manage features using SageMaker Feature Store; validate and label data using SageMaker Ground Truth / Mechanical Turk
  • Ensure data integrity: identify and mitigate bias (class imbalance, DPL) using SageMaker Clarify
  • Apply data classification, anonymization, masking and encryption for compliance (PII, PHI, data residency)
  • Prepare data for modeling (splitting, shuffling, augmentation) and configure data loading into training resources (EFS, FSx)

Domain 2 Domain 2: ML Model Development (26%)

  • Choose a modeling approach: assess feasibility, select ML algorithms/AI services (Bedrock, Rekognition, Translate, Transcribe), consider interpretability and cost
  • Use SageMaker built-in algorithms, script mode (TensorFlow/PyTorch), and JumpStart/Bedrock foundation models for fine-tuning
  • Train and refine models: hyperparameter tuning (SageMaker AMT), regularization (dropout, L1/L2), prevent overfitting/underfitting/catastrophic forgetting
  • Combine models via ensembling, stacking, boosting; reduce model size via pruning/compression/quantized data types
  • Manage model versions using SageMaker Model Registry for repeatability and audits
  • Analyze model performance: select/interpret evaluation metrics (F1, precision/recall, RMSE, ROC/AUC), create baselines, detect bias and convergence issues via SageMaker Clarify/Model Debugger

Domain 3 Domain 3: Deployment and Orchestration of ML Workflows (22%)

  • Select deployment infrastructure: real-time/serverless/asynchronous endpoints vs batch inference, compute provisioning (CPU/GPU), containers, edge optimization (SageMaker Neo)
  • Choose deployment orchestrator and target (SageMaker Pipelines, Airflow, SageMaker endpoints, ECS/EKS, Lambda) and deployment strategy (real time vs batch, blue/green, canary, linear)
  • Create and script infrastructure as code (CloudFormation, AWS CDK) including containerization (ECR, EKS, ECS, bring-your-own-container) and SageMaker endpoint auto scaling
  • Configure SageMaker endpoints within a VPC and deploy/host models using the SageMaker SDK
  • Set up CI/CD pipelines with AWS CodePipeline, CodeBuild, and CodeDeploy, integrated with Git-based version control
  • Automate orchestration of training/inference jobs (EventBridge rules, SageMaker Pipelines) and build automated tests plus retraining mechanisms

Domain 4 Domain 4: ML Solution Monitoring, Maintenance, and Security (24%)

  • Monitor model inference: detect data/model drift and anomalies using SageMaker Model Monitor and SageMaker Clarify; monitor performance via A/B testing
  • Monitor and optimize infrastructure and costs using CloudWatch, X-Ray, CloudTrail, Cost Explorer, Trusted Advisor, and resource tagging strategies
  • Rightsize instances and troubleshoot latency/scaling/capacity issues using SageMaker Inference Recommender and AWS Compute Optimizer
  • Optimize infrastructure costs via purchasing options (Spot, On-Demand, Reserved Instances, SageMaker Savings Plans)
  • Secure AWS resources: configure least-privilege IAM roles/policies for ML systems and applications, including SageMaker Role Manager
  • Build VPCs, subnets, and security groups to isolate ML systems; monitor, audit, and log ML systems for continued security and compliance

Course Info

Promotion Code

Your will get 10% discount voucher for 2nd course onwards if you write us a Google review.

Minimum Entry Requirement

Knowledge and Skills

  • Able to operate using computer functions
  • Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)

Attitude

  • Positive Learning Attitude
  • Enthusiastic Learner

Experience

  • Minimum of 1 year of working experience.

Target Age Group: 18-65 years old

Minimum Software/Hardware Requirement

Software:

TBD

Hardware: Window or Mac Laptops

Job Roles

Job Roles

  • Machine Learning Engineer
  • AI Solutions Architect
  • Data Scientist
  • Cloud AI Engineer
  • AWS Machine Learning Specialist
  • Deep Learning Engineer
  • Data Analyst (Machine Learning Focus)
  • AI Research Scientist
  • Computer Vision Engineer
  • Natural Language Processing Engineer
  • AWS Data Engineer
  • ML Operations Engineer
  • Predictive Analytics Consultant
  • Cloud Solutions Architect (ML Focus)
  • Robotics Process Automation Engineer
  • Model Deployment Engineer
  • AI Product Manager
  • Data Engineer (AI/ML Focus)
  • Cloud Developer (Machine Learning)
  • Technical Consultant (AI and ML)

Trainers

Trainers

Saeid is co-founder of Skymics Sdn Bhd. He has 8 years of experience in the field of IoT (Internet of Things) and Information Technology. He is a certified IBM IoT Practitioner and instructor, and a Certified Citizen Data Scientist Train-The-Trainer. He has been co-inventor of 3 inventions during the last 4 years.

Review

Customer Reviews (9)

Clear and easy to follow Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
The course materials were detailed and easy to reference afterwards. Great value for money. (Posted on 25/03/2026)
Good course materials Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
I found the course extremely useful and relevant to my job. Highly recommend it to others. (Posted on 01/05/2025)
Excellent course Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Great course materials and well-paced lessons. The exercises really helped me understand the topic. (Posted on 06/11/2024)
Very informative Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Really enjoyed the training. The examples were relevant and the pace was just right. (Posted on 26/08/2024)
Clear and easy to follow Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Very informative and interactive. The small class size meant we got a lot of personal attention. (Posted on 24/08/2024)

Items 1 to 5 of 9 total

per page
Page:
  1. 1
  2. 2

Write Your Own Review

You're reviewing: AWS Certified Machine Learning Engineer Associate Training

How do you rate this product? *

  1 star 2 stars 3 stars 4 stars 5 stars
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment