Exam Prep: AWS Certified Machine Learning Engineer - Associate

Length 1 day

Course overview

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Why study this course

Exam Prep: AWS Certified Machine Learning Engineer - Associate (MLA-C01) is a one-day course 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, operationalise, 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 exam style 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 recognise 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.

This course includes subject overview presentations, exam-style questions, use cases, and group discussions and activities.

Please note: The exam is not included in the course fee but can be purchased separately. Please contact us for a quote.

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What you’ll learn

This AWS training course is designed to teach participants how 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

AWS at Lumify Work

Lumify Work is an official AWS Training Partner for Australia, New Zealand, and the Philippines. Through our Authorised AWS Instructors, we can provide you with a learning path that’s relevant to you and your organisation, so you can get more out of the cloud. We offer virtual and face-to-face classroom-based training to help you build your cloud skills and enable you to achieve industry-recognised AWS Certification.

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Who is the course for?

This course is intended for individuals who are preparing for the AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam.

Course subjects

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
Analyse 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 interference
Monitor and optimise infrastructure costs
Secure AWS resources

Please note: This is an emerging technology course. Course outline is subject to change as needed.

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:

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:

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

Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.