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AWS Certified Machine Learning Engineer - Associate
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Course Description
Overview
The AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam validates a candidate's ability to build, operationalize, deploy, and maintain machine learning (ML) solutions and pipelines by using the AWS Cloud.The exam also validates a candidate's ability to complete the following tasks:
- Ingest, transform, validate, and prepare data for ML modeling.
- Select general modeling approaches, train models, tune hyperparameters, analyze model performance, and manage model versions.
- Choose deployment infrastructure and endpoints, provision compute resources, and configure auto scaling based on requirements.
- Set up continuous integration and continuous delivery (CI/CD) pipelines to automate orchestration of ML workflows.
- Monitor models, data, and infrastructure to detect issues.
- Secure ML systems and resources through access controls, compliance features, and best practices.
Objectives
Audience
Prerequisites
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The target candidate should have the following general IT knowledge:
- 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 CI/CD pipelines and infrastructure as code (IaC)
- Experience with code repositories for version control and CI/CD pipelines
- Knowledge of SageMaker 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
Topics
- Task 1.1: Ingest and store data
- Task 1.2: Transform data and perform feature engineering
- Task 1.3: Ensure data integrity and prepare data for modeling
- Task 2.1: Choose a modeling approach
- Task 2.2: Train and refine models
- Task 2.3: Analyze model performance
- Task 3.1: Select deployment infrastructure based on existing architecture and requirements
- Task 3.2: Create and script infrastructure based on existing architecture and requirements
- Task 3.3: Use automated orchestration tools to set up continuous integration and continuous delivery (CI/CD) pipelines
- Task 4.1: Monitor model inference
- Task 4.2: Monitor and optimize infrastructure and costs
- Task 4.3: Secure AWS resources
Related Courses
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Self-Paced Training Terms & Conditions
THIS IS A SELF-PACED VIRTUAL CLASS. AFTER YOU REGISTER, YOU HAVE 30 DAYS TO COMPLETE THE COURSE.
Before you enroll, review the system requirements to ensure that your system meets the minimum requirements for this course. AFTER YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT. You are billed for the course when you submit the enrollment form. Self-Paced Virtual Classes are non-refundable. Once you purchase a Self-Paced Virtual Class, you will be charged the full price.
After you receive confirmation that you are enrolled, you will be sent further instructions to access your course material and remote labs. A confirmation email will contain your online link, your ID and password, and additional instructions for starting the course.
You can start the course at any time within 12 months of enrolling for the course. After you register/start the course, you have 30 days to complete your course. Within this 30 days, the self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.
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