Machine Learning Engineering on AWS (AWS-MLMLEA)

Machine Learning (ML) Engineering on Amazon Web Services (AWS) is a 3-day intermediate course designed for ML professionals seeking to learn machine learning engineering on AWS. Participants learn to build, deploy, orchestrate, and operationalize ML solutions at scale through a balanced combination of theory, practical labs, and activities.Participants will gain practical experience using AWS services such as Amazon SageMaker AI and analytics tools such as Amazon EMR to develop robust, scalable, and production-ready machine learning applications. Course Objectives:In this course, you will learn to do the following:Explain ML fundamentals and its applications in the AWS Cloud.Process, transform, and engineer data for ML tasks by using AWS services.Select appropriate ML algorithms and modeling approaches based on problem requirements and model interpretability.Design and implement scalable ML pipelines by using AWS services for model training, deployment, and orchestration.Create automated continuous integration and delivery (CI/CD) pipelines for ML workflows.Discuss appropriate security measures for ML resources on AWS.Implement monitoring strategies for deployed ML models, including techniques for detecting data drift. Prerequisites:We recommend that attendees of this course have the following:Familiarity with basic machine learning conceptsWorking knowledge of Python programming language and common data science libraries such as NumPy, Pandas, and Scikit-learnBasic understanding of cloud computing concepts and familiarity with AWSExperience with version control systems such as Git (beneficial but not required)