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

Course Summary

Exam Prep: AWS Certified Machine Learning Engineer – Associate (MLA-C01) is a one-day ILT where you learn how to assess your preparedness for the AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam. The exam validates a candidate’s ability to build, operationalize, and maintain machine learning (ML) solutions and pipelines by using the AWS Cloud.

This 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.

Prerequisites:
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 protection

THIS COURSE IS NOT ELIGIBLE FOR TRAINING BUNDLES.

Purpose	Learn to build, deploy, and maintain ML solutions while gaining confidence for the MLA-C01 certification exam
Audience	Individuals who are preparing for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam
Role	Cloud architects \| Solutions architects \| Anyone who designs solutions for cloud infrastructures
Skill level	Intermediate
Duration	1 Day
Related technologies	Cloud Computing Training \| AWS \| Machine Learning

Course Objectives

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

What you'll learn:

In this course, you'll learn:

Domain 1: Data Preparation for Machine Learning (ML)

Ingest and store data.
Transform data and perform feature engineering.
Ensure data integrity and prepare data for modeling.

Domain 2: ML Model Development

Choose a modeling approach.
Train and refine models.
Analyze model performance.

Domain 3: Deployment and Orchestration of ML Workflows

Select deployment infrastructure based on existing architecture and requirements.
Create and script infrastructure based on existing architecture and requirements.
Use automated orchestration tools to set up continuous integration and continuous delivery (CI/CD) pipelines.

Domain 4: ML Solution Monitoring, Maintenance, and Security

Monitor model inference.
Monitor and optimize infrastructure costs.
Secure AWS resources.