{"product_id":"aws-certified-machine-learning-engineer","title":"Machine Learning Engineering on AWS","description":"\u003cdiv\u003e\u003cp\u003eThis 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 examstyle 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.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eMachine Learning Engineering on AWS Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eUpon completing this course, students should be able to:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eIdentify the scope and content tested by the AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam.\u003c\/li\u003e\n\u003cli\u003ePractice exam-style questions and evaluate your preparation strategy.\u003c\/li\u003e\n\u003cli\u003eExamine use cases and differentiate between them.\u003cb\u003e\u003c\/b\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cb\u003ePrerequisites\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eLearners are recommended to have the following:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eSuggested 1 year of experience in a related role such as a backend software developer, DevOps developer, data engineer, or data scientist.\u003c\/li\u003e\n\u003cli\u003eBasic understanding of common ML algorithms and their use cases\u003c\/li\u003e\n\u003cli\u003eData engineering fundamentals, including knowledge of common data formats, ingestion, and transformation to work with ML data pipelines\u003c\/li\u003e\n\u003cli\u003eKnowledge of querying and transforming data\u003c\/li\u003e\n\u003cli\u003eKnowledge of software engineering best practices for modular, reusable code development, deployment, and debugging\u003c\/li\u003e\n\u003cli\u003eFamiliarity with provisioning and monitoring cloud and on-premises ML resources\u003c\/li\u003e\n\u003cli\u003eExperience with continuous integration and continuous delivery (CI\/CD) pipelines and infrastructure as code (IaC)\u003c\/li\u003e\n\u003cli\u003eExperience with code repositories for version control and CI\/CD pipelines.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003eLearners are recommended to be able to do the following:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eSuggested 1 year of experience using Amazon SageMaker AI and other AWS services for ML engineering.\u003c\/li\u003e\n\u003cli\u003eKnowledge of Amazon SageMaker AI capabilities and algorithms for model building and deployment\u003c\/li\u003e\n\u003cli\u003eKnowledge of AWS data storage and processing services for preparing data for modeling\u003c\/li\u003e\n\u003cli\u003eFamiliarity with deploying applications and infrastructure on AWS\u003c\/li\u003e\n\u003cli\u003eKnowledge of monitoring tools for logging and troubleshooting ML systems\u003c\/li\u003e\n\u003cli\u003eKnowledge of AWS services for the automation and orchestration of CI\/CD pipelines\u003c\/li\u003e\n\u003cli\u003eUnderstanding of AWS security best practices for identity and access management, encryption, and data protection\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eMachine Learning Engineering on AWS Course Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eLearning Objectives\u003c\/h4\u003e\n\u003cp\u003e\u003cb\u003eDomain 1: \u003c\/b\u003eData Preparation for Machine Learning (ML)\u003c\/p\u003e\n\u003cp\u003e1.1 Ingest and store data.\u003c\/p\u003e\n\u003cp\u003e1.2 Transform data and perform feature engineering.\u003c\/p\u003e\n\u003cp\u003e1.3 Ensure data integrity and prepare data for modeling\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eDomain 2:\u003c\/b\u003e ML Model Development\u003c\/p\u003e\n\u003cp\u003e2.1 Choose a modeling approach.\u003c\/p\u003e\n\u003cp\u003e2.2 Train and refine models.\u003c\/p\u003e\n\u003cp\u003e2.3 Analyze model performance\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eDomain 3\u003c\/b\u003e: Deployment and Orchestration of ML Workflows\u003c\/p\u003e\n\u003cp\u003e3.1 Select deployment infrastructure based on existing architecture and requirements.\u003c\/p\u003e\n\u003cp\u003e3.2 Create and script infrastructure based on existing architecture and requirements.\u003c\/p\u003e\n\u003cp\u003e3.3 Use automated orchestration tools to set up continuous integration and continuous delivery (CI\/CD) pipelines.\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eDomain 4:\u003c\/b\u003e ML Solution Monitoring, Maintenance, and Security\u003c\/p\u003e\n\u003cp\u003e4.1 Monitor model interference.\u003c\/p\u003e\n\u003cp\u003e 4.2 Monitor and optimize infrastructure costs.\u003c\/p\u003e\n\u003cp\u003e4.3 Secure AWS resources.\u003c\/p\u003e\n\u003cp\u003e\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Learning Tree","offers":[{"title":"268A60US \/ 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