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AWS Certified Machine Learning Specialty Training

Course Information

  • Sessions 5 days
  • Duration 37.5 hrs
  • Level Advanced
  • 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

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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 Specialty Training

Course Code: C279

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What's This Course About

AWS Certified Machine Learning Specialty MLS-C01 Exam Prep is designed to equip you with the necessary skills and knowledge to pass the MLS-C01 exam confidently. This course covers key topics such as data engineering, exploratory data analysis, modeling, machine learning implementation, and operations. Our expert instructors provide in-depth instruction and practical insights, ensuring you grasp complex concepts and apply them effectively.

With a focus on real-world applications, our course includes hands-on labs, practice exams, and interactive learning modules. You will learn to use AWS machine learning services, such as SageMaker, to build, train, and deploy models. By the end of the course, you will be well-prepared to tackle the MLS-C01 exam and advance your career in the field of machine learning.

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 7,000.00 Before GST
GST 630.00 9% of fee
Baseline Nett 4,130.00 SG/PR age 21+ · 50% funded
MCES / SME Nett 2,730.00 SG age 40+ · 70% funded
Funding and Grant Applications

No funding is available for this course

Course Fee

MYR7,000.00

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 Data Preparation for Machine Learning (ML) (28%)

  • Ingest and store data from AWS sources (S3, EFS, FSx, Kinesis, Kafka, Flink) in formats like Parquet, JSON, CSV, ORC, Avro, RecordIO
  • Merge data from multiple sources using AWS Glue, Apache Spark, or SageMaker Data Wrangler
  • Transform data and perform feature engineering (scaling, binning, log transform, encoding, tokenization)
  • Validate and label data using SageMaker Ground Truth or Amazon Mechanical Turk
  • Identify and mitigate bias (class imbalance, difference in proportions of labels) using SageMaker Clarify
  • Ensure data integrity, encryption, classification, and compliance (PII/PHI, data residency) before modeling

Domain 2 ML Model Development (26%)

  • Choose a modeling approach: compare ML algorithms, SageMaker built-in algorithms, AI services (Rekognition, Transcribe, Bedrock), and foundation models via SageMaker JumpStart
  • Train models using SageMaker built-in algorithms, script mode with TensorFlow/PyTorch, and fine-tune pre-trained models
  • Tune hyperparameters (random search, Bayesian optimization) via SageMaker Automatic Model Tuning
  • Prevent overfitting/underfitting/catastrophic forgetting via regularization, ensembling, stacking, boosting
  • Manage model versions and repeatability using SageMaker Model Registry
  • Evaluate model performance using metrics (F1, precision, recall, RMSE, ROC/AUC, confusion matrix) and SageMaker Clarify/Model Debugger

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

  • Select deployment infrastructure and endpoint types (real-time, serverless, asynchronous, batch) based on cost/latency/performance tradeoffs
  • Choose deployment targets (SageMaker endpoints, ECS, EKS, Lambda) and containers (built-in vs. BYOC)
  • Provision and script infrastructure as code (CloudFormation, AWS CDK) and configure SageMaker endpoint auto scaling
  • Optimize edge deployments using SageMaker Neo
  • Set up CI/CD pipelines with CodePipeline, CodeBuild, and CodeDeploy for ML workflow automation
  • Apply deployment/rollback strategies (blue/green, canary, linear) and build automated retraining and testing mechanisms

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

  • Monitor model inference in production for drift, data quality, and anomalies using SageMaker Model Monitor and Clarify
  • Conduct A/B testing and compare shadow vs. production variant performance
  • Monitor and optimize infrastructure and cost using CloudWatch, X-Ray, CloudTrail, Cost Explorer, and Trusted Advisor
  • Rightsize instances and select purchasing options (Spot, On-Demand, Reserved, SageMaker Savings Plans)
  • Secure ML resources via IAM least-privilege policies, roles, and SageMaker Role Manager
  • Build VPCs, subnets, and security groups to isolate ML systems and secure CI/CD pipelines

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:

Hardware: Window or Mac Laptops

Job Roles

Job Roles

  • Machine Learning Engineer
  • Data Scientist
  • AI/ML Specialist
  • Cloud Solutions Architect
  • Data Engineer
  • AWS Solutions Architect
  • Research Scientist
  • AI Developer
  • Big Data Engineer
  • Analytics Engineer
  • Cloud Engineer
  • DevOps Engineer
  • Data Analyst
  • Software Engineer
  • Business Intelligence Developer
  • IT Consultant
  • System Architect
  • Application Developer
  • Technical Consultant
  • Product Manager in AI/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 (5)

Exceeded my expectations Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
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A very practical course. The trainer made a complex topic simple and enjoyable to learn. (Posted on 17/05/2026)
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 25/09/2024)
Very satisfied 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 engaging session. The trainer patiently answered all our questions and gave useful tips. (Posted on 08/06/2024)
Practical and relevant 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 trainer was knowledgeable and explained the concepts clearly with real-world examples. (Posted on 21/04/2024)
Knowledgeable trainer 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
Solid content and a supportive trainer. I would definitely sign up for more courses here. (Posted on 20/01/2024)

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