AWS Certified Machine Learning Engineer - Associate
This course covers AWS machine learning workflows for the AWS Certified Machine Learning Engineer, Associate certification. Participants study data preparation, feature engineering, model training, evaluation, deployment, monitoring, and responsible AI practices.
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Description
This course covers AWS machine learning workflows for the AWS Certified Machine Learning Engineer, Associate certification. Participants study data preparation, feature engineering, model training, evaluation, deployment, monitoring, and responsible AI practices. Teaching typically combines lectures, demonstrations, hands-on labs, and case studies using AWS services such as Amazon SageMaker, Amazon S3, AWS Glue, and AWS Lambda. The course balances core ML concepts with practical AWS tasks, helping participants prepare for the exam and apply machine learning workflows in cloud-based projects.
What You Will Learn
Module 1: Data preparation for machine learning
Ingest and store data using Amazon S3, Amazon EFS, Amazon FSx, Amazon RDS, Amazon DynamoDB, Amazon Kinesis, Apache Kafka, and Apache Flink.
Compare formats such as CSV, JSON, Apache Parquet, Apache ORC, Apache Avro, and RecordIO based on access patterns and model needs.
Clean, label, transform, and validate data with AWS Glue, AWS Glue DataBrew, SageMaker Data Wrangler, SageMaker Feature Store, SageMaker Ground Truth, and Amazon Mechanical Turk.
Module 2: Model development and evaluation
Select suitable ML approaches, SageMaker AI built-in algorithms, foundation models, SageMaker JumpStart templates, and Amazon Bedrock options for defined business problems.
Train and refine models using TensorFlow, PyTorch, SageMaker AI script mode, hyperparameter tuning, regularization, pruning, ensembling, and version control with SageMaker Model Registry.
Assess performance with confusion matrices, F1 score, precision, recall, RMSE, ROC, AUC, SageMaker Clarify, and SageMaker Model Debugger.
Module 3: Deployment and workflow orchestration
Deploy models for real-time inference, batch inference, asynchronous endpoints, serverless endpoints, and multi-model use cases.
Work with SageMaker AI endpoints, Amazon ECS, Amazon EKS, AWS Lambda, Amazon ECR, AWS CDK, AWS CloudFormation, SageMaker Pipelines, Apache Airflow, AWS CodePipeline, AWS CodeBuild, and AWS CodeDeploy.
Module 4: Monitoring, maintenance, and security
Monitor drift, latency, data quality, model behavior, and infrastructure health with SageMaker Model Monitor, Amazon CloudWatch, AWS X-Ray, AWS CloudTrail, Amazon EventBridge, and Amazon QuickSight.
Apply IAM roles and policies, encryption, VPC settings, security groups, AWS KMS, AWS Secrets Manager, tagging, AWS Cost Explorer, AWS Budgets, and AWS Trusted Advisor.
Certification & Exam
This course prepares participants for the AWS Certified Machine Learning Engineer, Associate certification exam, MLA-C01.
Candidates take a 130-minute exam with 65 questions, delivered through Pearson VUE at a test center or as an online proctored exam. Question formats include multiple choice, multiple response, ordering, and matching. AWS scores 50 questions; 15 unscored questions are included but are not identified during the exam.
Results are pass or fail, reported on a scaled score from 100 to 1,000, with 720 as the minimum passing score. Passing the exam earns the AWS Certified Machine Learning Engineer, Associate credential, valid for 3 years. Reference: official AWS certification page.
What You Will Achieve
After completing this training, participants will be able to:
Prepare machine learning data for modeling by selecting storage formats such as CSV, JSON, Parquet, ORC, and Avro, ingesting data from AWS sources, and using services such as Amazon S3, AWS Glue, Amazon Kinesis, SageMaker Data Wrangler, and SageMaker Feature Store.
Transform datasets through cleaning, deduplication, missing-value handling, outlier treatment, scaling, normalization, encoding, and feature engineering for structured and unstructured ML use cases.
Validate data quality and reduce data-related risk by checking integrity, identifying bias, applying dataset splitting and augmentation methods, and using tools such as AWS Glue Data Quality, AWS Glue DataBrew, SageMaker Clarify, and SageMaker Ground Truth.
Select suitable machine learning approaches by comparing algorithms, SageMaker built-in algorithms, foundation models, SageMaker JumpStart options, Amazon Bedrock, and AWS AI services such as Amazon Rekognition, Amazon Transcribe, Amazon Translate, and Amazon Comprehend.
Train and refine models using SageMaker, common ML libraries, TensorFlow, PyTorch, script mode, hyperparameter tuning, regularization, model versioning, and methods for reducing overfitting and underfitting.
Evaluate model performance using metrics such as accuracy, precision, recall, F1 score, RMSE, ROC, AUC, confusion matrices, bias metrics, and performance baselines to compare model quality, training time, and cost.
Deploy machine learning models using appropriate AWS targets and patterns, including SageMaker endpoints, batch inference, real-time inference, asynchronous inference, serverless endpoints, containers, Amazon ECS, Amazon EKS, AWS Lambda, and VPC-based configurations.
Implement operational practices for ML workloads by setting up CI/CD pipelines with AWS CodePipeline, CodeBuild, and CodeDeploy, monitoring with SageMaker Model Monitor, Amazon CloudWatch, AWS CloudTrail, and AWS X-Ray, and applying IAM, encryption, logging, and cost controls.
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