Exam Prep: AWS Certified Machine Learning Engineer -Associate (MLA-C01)

Name: Exam Prep: AWS Certified Machine Learning Engineer -Associate (MLA-C01)
Price: 695.00 USD
Availability: InStock
Rating: 4.5 (1200 reviews)

Price
$695.00 USD

Duration
1 Day

Delivery Methods
Virtual Instructor Led
Private Group

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Course Overview

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 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 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.

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.

Who Should Attend?

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

Top-rated instructors: Our crew of subject matter experts have an average instructor rating of 4.8 out of 5 across thousands of reviews.
Authorized content: We maintain more than 35 Authorized Training Partnerships with the top players in tech, ensuring your course materials contain the most relevant and up-to date information.
Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
Private Group Training: Let our world-class instructors deliver exclusive training courses just for your employees. Our private group training is designed to promote your team’s shared growth and skill development.
Tailored Training Solutions: Our subject matter experts can customize the class to specifically address the unique goals of your team.

Course Prerequisites

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

Agenda

Domain 1: Data Preparation for Machine Learning (ML)

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

Domain 2: ML Model Development

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

Domain 3: Deployment and Orchestration of ML Workflows

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