---
url: "https://xcademia.com/courses/aws-ml-engineer-associate-training"
title: " AWS ML Engineer Associate Training"
description: "Learn AWS machine learning in this 4-day mentor-led course aligned to MLA-C01. Build, train, and deploy ML models using SageMaker."
publishedAt: "2026-03-20T10:24:08.805818+00:00"
updatedAt: "2026-03-30T22:50:53.7265+00:00"
type: course
code: "CLD-0097"
level: Professional
duration_days: "4"
track: AWS Tracks
category: "Cloud & DevOps"
credential_tier: tier1
price_gbp: "2199"
---

#  AWS ML Engineer Associate Training

> Build and deploy machine learning solutions on AWS aligned with the ML Engineer Associate (MLA-C01) objectives. Learn through mentor-led sessions and practical scenarios using SageMaker and AWS AI services.

## Overview

This AWS ML Engineer Associate Training programme is designed for professionals looking to build, train, and deploy machine learning models on AWS. Aligned with the MLA-C01 certification objectives, the course provides a structured pathway into practical machine learning engineering in cloud environments.

Through mentor-led sessions and practical scenarios, learners will prepare datasets, train models, and deploy scalable ML solutions using Amazon SageMaker and other AWS AI services. The course focuses on real-world workflows including feature engineering, model evaluation, and production deployment.

By the end of the programme, participants will be able to implement end-to-end ML pipelines, manage model lifecycle processes, and optimise performance and cost in AWS environments. This course supports professionals in data science, ML engineering, and AI development roles.

## Prerequisites

- Basic understanding of machine learning concepts
- Familiarity with AWS services
- Knowledge of Python or data tools

## What you will learn

- Design end-to-end ML pipelines
- Implement model training and deployment
- Analyse model performance and accuracy
- Evaluate data preparation techniques
- Manage ML lifecycle and operations
- Communicate ML solutions effectively

## Skills you will gain

- Data preparation techniques
- SageMaker model training
- ML model deployment
- Feature engineering basics
- MLOps workflow design
- ML performance optimisation

## Career progression

- Machine Learning Engineer
- AI Engineer
- Data Scientist
- Cloud AI Engineer
- MLOps Engineer

## Curriculum

1. **Module 1**
2. **Module 2**
3. **Module 3**
4. **Module 4**

## Exam & certification

This programme is aligned with the official exam objectives. Exam registration and certification are managed directly by the awarding body.

## Delivery options

- **Live Online** — Join live instructor-led sessions from anywhere. Interactive, engaging, and flexible.
- **Onsite Training** — We come to you. Training delivered at your workplace for teams of 6 or more.
- **Venue-Based** — Classroom training at a professional venue. Ideal for focused, immersive learning.
- **Blended** — Combine online and in-person learning for maximum flexibility and impact.

## Frequently asked questions

**Is this course aligned with AWS ML Engineer Associate certification?**

Yes, it is aligned with the MLA-C01 exam objectives.



**Do I need prior machine learning experience?**

Basic understanding of ML concepts is recommended.



**Are hands-on labs included?**

Yes, practical labs are included using SageMaker and AWS services.



**Does this course need an exam?**

No, the exam is optional and can be taken separately.



**What roles can I pursue after this course?**

Roles include ML Engineer, AI Engineer, and Data Scientist.

## Course at a glance

| Field | Value |
| --- | --- |
| Code | CLD-0097 |
| Duration | 4 days |
| Level | Professional |
| Track | AWS Tracks |
| Category | Cloud & DevOps |
| Credential tier | tier1 |
| Price (GBP) | £2199 |

---

## About this content

This Markdown course profile is the citation-grade twin of [ AWS ML Engineer Associate Training](https://xcademia.com/courses/aws-ml-engineer-associate-training). It is published by **Xcademia** (UK Companies House 12322710) and is available for AI search engines and large language models to index, summarise, and cite.

When citing or quoting, please attribute *Xcademia* and link back to the source URL above.

- Source: https://xcademia.com/courses/aws-ml-engineer-associate-training
- Publisher: Xcademia — https://xcademia.com
- Catalogue index: https://xcademia.com/llms-full.txt