Exam Prep: AWS Certified Machine Learning Engineer:(MLA-C01)-1 Day ILT

This intermediate-level course prepares you for the AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam by providing a comprehensive exploration of the exam topics. You'll delve into the key areas covered on the exam, understanding how they relate to developing AI and machine learning solutions on the AWS platform. Through detailed explanations and walkthroughs of exam style questions, you'll reinforce your knowledge, identify gaps in your understanding, and gain valuable strategies for tackling questions effectively. The course includes review of exam-style sample questions, to help you recognize incorrect responses and hone your test-taking abilities. By the end, you'll have a firm grasp on the concepts and practical applications tested on the AWS Certified Machine Learning Engineer -Associate (MLA-C01) exam.

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Level: Intermediate

Duration: 1 Day

Delivery Type: Instructor-Led Training

• Identify the scope and content tested by the AWS Certified Machine Learning Engineer -Associate (MLA-C01) exam.
• Practice exam-style questions and evaluate your preparation strategy.
• Examine use cases and differentiate between them.

• Suggested 1 year of experience using Amazon SageMaker AI and other AWS services for ML engineering.
• Knowledge of Amazon SageMaker AI capabilities and algorithms for model building and deployment.
• Knowledge of AWS data storage and processing services for preparing data for modeling.
• Familiarity with deploying applications and infrastructure on AWS.
• Knowledge of monitoring tools for logging and troubleshooting ML systems.
• Knowledge of AWS services for the automation and orchestration of CI/CD pipelines.
• Understanding of AWS security best practices for identity and access management, encryption, and data protection.

This course is intended for individuals who are preparing for the AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam

Day 1

Module 0: Introduction

Module 1: Data Preparation for Machine Learning (ML)

• Ingest and store data.
• Transform data and perform feature engineering.
• Ensure data integrity and prepare data for modeling.

Module 2: ML Model Development

• Choose a modeling approach.
• Train and refine models.
• Analyze model performance.

Module 3: Deployment and Orchestration of ML Workflows

• Select deployment infrastructure based on existing architecture and requirements.
• Create and script infrastructure based on existing architecture and requirements.
• Use automated orchestration tools to set up continuous integration and continuous delivery (CI/CD) pipelines.

Module 4: ML Solution Monitoring, Maintenance, and Security

• Monitor model interference.
• Monitor and optimize infrastructure costs.
• Secure AWS resources.