TGS-2024049340 - AWS Certified Machine Learning Engineer Associate Training

Course: AWS Certified Machine Learning Engineer Associate Training
Course Code: TGS-2024049340
Register here: https://www.tertiarycourses.com.sg/wsq-aws-certified-machine-learning-engineer-associate-training.html

Hands-on AWS Certified Machine Learning Engineer Associate labs for learners preparing for the AWS Certified Machine Learning Engineer - Associate exam. The labs cover data preparation, feature engineering, data quality, model development, training, tuning, evaluation, deployment, orchestration, monitoring, security, cost optimization, and exam-style review.

Courseware

Item	Description
Learner Guide	Detailed step-by-step guide for completing the labs and preparing final deliverables.
Lab Guide	Lab catalogue grouped by AWS ML engineering skill area.
Tools Reference	Recommended AWS tools, cost-safety notes, and learner setup checklist.

How to Use

1. Create or use an AWS account provided by your trainer.
2. Create a working folder named TGS-2024049340-AWS-MLA-Labs.
3. Complete the labs in sequence because later labs reuse S3, SageMaker, IAM, model registry, endpoints, monitoring, and pipeline concepts.
4. Record screenshots, notebooks, SQL snippets, feature notes, model metrics, and configuration notes for each lab.
5. Delete paid resources when the lab asks you to clean up.

Lab Catalogue

Domain 1 - Data Preparation for Machine Learning

Lab	Title	Focus
Lab 1	Account Safety, IAM, S3 ML Data Lake, and Data Formats	IAM, S3, encryption, CSV/JSON/Parquet/ORC, SageMaker access
Lab 2	Data Ingestion, Transformation, Feature Engineering, and Data Quality	Glue, Data Wrangler, DataBrew, Feature Store, bias and quality checks

Domain 2 - ML Model Development

Lab	Title	Focus
Lab 3	Modeling Approach, SageMaker Algorithms, JumpStart, and AI Services	Algorithm selection, SageMaker built-ins, Bedrock, Rekognition, Translate, Transcribe
Lab 4	Training, Hyperparameter Tuning, and Model Registry	Training jobs, script mode, AMT, regularization, model versions, registry
Lab 5	Model Evaluation, Performance Metrics, and Bias Analysis	Confusion matrix, F1, ROC/AUC, overfitting, Clarify, explainability

Domain 3 - Deployment, Orchestration, Monitoring, and Security

Lab	Title	Focus
Lab 6	Deployment Infrastructure, Endpoints, Batch Transform, and Auto Scaling	Real-time endpoints, serverless inference, batch transform, autoscaling, containers
Lab 7	MLOps Pipelines, CI/CD, and Workflow Orchestration	SageMaker Pipelines, Step Functions, CodePipeline, model approval, IaC
Lab 8	Monitoring, Security, Cost Optimization, and Exam Review	Model Monitor, CloudWatch, data drift, IAM, KMS, VPC, cost, timed review

Reference

• AWS Certified Machine Learning Engineer - Associate exam guide: https://docs.aws.amazon.com/pdfs/aws-certification/latest/machine-learning-engineer-associate-01/machine-learning-engineer-associate-01.pdf
• Course registration: https://www.tertiarycourses.com.sg/wsq-aws-certified-machine-learning-engineer-associate-training.html
• AWS Documentation: https://docs.aws.amazon.com/
• Amazon SageMaker AI documentation: https://docs.aws.amazon.com/sagemaker/

Free Tools Used

• AWS Free Tier eligible services where possible
• AWS Management Console
• Amazon SageMaker AI
• Amazon S3
• AWS Glue
• Amazon Athena
• Amazon CloudWatch
• AWS Step Functions
• AWS Pricing Calculator