Exam Prep: AWS Certified Machine Learning Engineer

Exam Prep: AWS Certified Machine Learning Engineer – Associate (MLA-C01)EPCMLEAAWAmazon Web ServicesAW-EPCMLEA1.0In this course, you will learn to: <ul> <li>Identify the scope and content tested by the AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam.</li><li>Practice exam-style questions and evaluate your preparation strategy.</li><li>Examine use cases and differentiate between them.</li></ul>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: <ul> <li>Suggested 1 year of experience in a related role such as a backend software developer, DevOps developer, data engineer, or data scientist.</li><li>Basic understanding of common ML algorithms and their use cases</li><li>Data engineering fundamentals, including knowledge of common data formats, ingestion, and transformation to work with ML data pipelines</li><li>Knowledge of querying and transforming data</li><li>Knowledge of software engineering best practices for modular, reusable code development, deployment, and debugging</li><li>Familiarity with provisioning and monitoring cloud and on-premises ML resources</li><li>Experience with continuous integration and continuous delivery (CI/CD) pipelines and infrastructure as code (IaC)</li><li>Experience with code repositories for version control and CI/CD pipelines</li></ul>Recommended AWS knowledge Learners are recommended to be able to do the following: <ul> <li>Suggested 1 year of experience using Amazon SageMaker AI and other AWS services for ML engineering.</li><li>Knowledge of Amazon SageMaker AI capabilities and algorithms for model building and deployment</li><li>Knowledge of AWS data storage and processing services for preparing data for modeling</li><li>Familiarity with deploying applications and infrastructure on AWS</li><li>Knowledge of monitoring tools for logging and troubleshooting ML systems</li><li>Knowledge of AWS services for the automation and orchestration of CI/CD pipelines</li><li>Understanding of AWS security best practices for identity and access management, encryption, and data protection</li></ul>This course is intended for individuals who are preparing for the AWS Certified Machine Learning Engineer - Associate (MLA-C01) examThis course includes subject overview presentations, exam-style questions, use cases, and group discussions and activities.Introduction Domain 1: Data Preparation for Machine Learning (ML) <ul> <li>1.1 Ingest and store data.</li><li>1.2 Transform data and perform feature engineering.</li><li>1.3 Ensure data integrity and prepare data for modeling.</li></ul>Domain 2: ML Model Development <ul> <li>2.1 Choose a modeling approach.</li><li>2.2 Train and refine models.</li><li>2.3 Analyze model performance.</li></ul>Domain 3: Deployment and Orchestration of ML Workflows <ul> <li>3.1 Select deployment infrastructure based on existing architecture and requirements.</li><li>3.2 Create and script infrastructure based on existing architecture and requirements.</li><li>3.3 Use automated orchestration tools to set up continuous integration and continuous delivery</li><li>(CI/CD) pipelines.</li></ul>Domain 4: ML Solution Monitoring, Maintenance, and Security <ul> <li>4.1 Monitor model interference.</li><li>4.2 Monitor and optimize infrastructure costs.</li><li>4.3 Secure AWS resources.</li></ul>Course completionIn this course, you will learn to: - 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.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: - Suggested 1 year of experience in a related role such as a backend software developer, DevOps developer, data engineer, or data scientist. - Basic understanding of common ML algorithms and their use cases - Data engineering fundamentals, including knowledge of common data formats, ingestion, and transformation to work with ML data pipelines - Knowledge of querying and transforming data - Knowledge of software engineering best practices for modular, reusable code development, deployment, and debugging - Familiarity with provisioning and monitoring cloud and on-premises ML resources - Experience with continuous integration and continuous delivery (CI/CD) pipelines and infrastructure as code (IaC) - Experience with code repositories for version control and CI/CD pipelines Recommended AWS knowledge Learners are recommended to be able to do the following: - 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 protectionThis course is intended for individuals who are preparing for the AWS Certified Machine Learning Engineer - Associate (MLA-C01) examThis course includes subject overview presentations, exam-style questions, use cases, and group discussions and activities.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 completion1 jour675.00930.00900.00750.00450.00750.00750.00750.00750.00750.00