Exam Prep: AWS Certified Machine Learning Engineer – Associate (MLA-C01)

In this course, you will learn to:

• the scope and content tested by the AWS Certified Machine Learning Engineer -

Associate (MLA-C01) exam.

• exam-style questions and evaluate your preparation strategy.
• use cases and differentiate between them.

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

You are not required to take any specific training before taking this course. However, the following prerequisite knowledge is recommended prior to taking the AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam. General IT knowledge Learners are recommended to have the following:
• 1 year of experience in a related role such as a backend software developer, DevOps developer, data engineer, or data scientist.
• understanding of common ML algorithms and their use cases
• engineering fundamentals, including knowledge of common data formats, ingestion, and transformation to work with ML data pipelines
• of querying and transforming data
• of software engineering best practices for modular, reusable code development, deployment, and debugging
• with provisioning and monitoring cloud and on-premises ML resources
• with continuous integration and continuous delivery (CI/CD) pipelines and infrastructure as code (IaC)
• with code repositories for version control and CI/CD pipelines
Recommended AWS knowledge Learners are recommended to be able to do the following:
• 1 year of experience using Amazon SageMaker AI and other AWS services for ML engineering.
• of Amazon SageMaker AI capabilities and algorithms for model building and deployment
• of AWS data storage and processing services for preparing data for modeling
• with deploying applications and infrastructure on AWS
• of monitoring tools for logging and troubleshooting ML systems
• of AWS services for the automation and orchestration of CI/CD pipelines
• of AWS security best practices for identity and access management, encryption, and data protection

Introduction Domain 1: Data Preparation for Machine Learning (ML) 1.1 Ingest and store data. 1.2 Transform data and perform feature engineering. 1.3 Ensure data integrity and prepare data for modeling. Domain 2: ML Model Development 2.1 Choose a modeling approach. 2.2 Train and refine models. 2.3 Analyze model performance. Domain 3: Deployment and Orchestration of ML Workflows 3.1 Select deployment infrastructure based on existing architecture and requirements. 3.2 Create and script infrastructure based on existing architecture and requirements. 3.3 Use automated orchestration tools to set up continuous integration and continuous delivery (CI/CD) pipelines. Domain 4: ML Solution Monitoring, Maintenance, and Security 4.1 Monitor model interference. 4.2 Monitor and optimize infrastructure costs. 4.3 Secure AWS resources. Course completion