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Instructor-led training
AWS Machine Learning Engineer Associate Preparation Training in London and Live Online

AWS Machine Learning Engineer Associate Preparation

Prepare for the AWS Machine Learning Engineer Associate pathway by building, deploying and operating machine learning workloads. Focus on data quality, reproducible pipelines and production monitoring.

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Intermediate

Python and machine learning experience plus practical knowledge of AWS storage, security and compute.

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Course overview
Course dates
  • In Classroom
  • Live Virtual
  • 1 - 1 Training
  • Corporate Training
  • Onsite Training
Introduction
Prepare for the AWS Machine Learning Engineer Associate pathway by building, deploying and operating machine learning workloads. Focus on data quality, reproducible pipelines and production monitoring.


This instructor-led course combines clear explanations with guided exercises and practical scenarios. Work through examples relevant to the subject, review alternative approaches and practise checking your results rather than relying on a demonstration alone.
You will develop a structured approach to implementation, troubleshooting and documentation. The trainer can adapt examples to the agreed learning needs and available training environment. Confirm the course duration, software or lab requirements, delivery arrangements and fees with London IT Training before booking.
The course supports preparation for AWS Certified Machine Learning Engineer – Associate. Training attendance does not award the credential. The awarding body sets separate examination, eligibility and experience requirements; confirm these and any exam fees before booking.
What the course covers
Flexible Ways to Learn

Study your way with flexible learning options designed to fit around your schedule.

Small Groups

With small groups of students, our instructors can work closely with each student

Flexible Class Schedules

Our class schedules are flexible on weekdays, weekend or evening to suit your schedule.

Experienced Instructors

Our instructors follow a modified and personalised approach to engage students during class

Hi-Tech lab Facilities

Lab arrangements vary by course. Confirm access and equipment requirements before booking.

Prerequisites
What you’ll gain

Apply data preparation for ml in a practical course exercise.

Apply model development and evaluation in a practical course exercise.

Apply deployment architecture in a practical course exercise.

Apply workflow orchestration in a practical course exercise.

Develop skills for your next step
Relevant career pathways
Technical Practitioner:

Apply the course skills to relevant technical tasks.

Technical Consultant:

Assess requirements and recommend practical implementation choices.

Syllabus

Course context and practical foundations
Review the course objectives and relevant concepts. Set up a suitable training environment, identify a realistic example and agree how to validate the results.

Select ingestion and preparation approaches. Validate quality, transformations and access controls for training data.

Configure training, tune experiments and compare models with metrics appropriate to the problem and its risks.

Choose inference patterns, integrate application access and evaluate scaling, availability and latency requirements.

Automate repeatable data and model stages. Track versions, failures, approvals and deployment dependencies.

Monitor model and service behaviour, investigate drift and failures and plan controlled updates or retraining.

Apply least privilege and data protection. Compare operational trade-offs and practise structured reasoning on exam-style scenarios.

Complete an integrated exercise using sample data or an isolated training environment. Test the result, document decisions and troubleshooting steps, and identify improvements and next learning objectives.

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FAQs

Who is this course for?
Prepare for the AWS Machine Learning Engineer Associate pathway by building, deploying and operating machine learning workloads. Focus on data quality, reproducible pipelines and production monitoring.

Python and machine learning experience plus practical knowledge of AWS storage, security and compute.

Data preparation for ML; Model development and evaluation; Deployment architecture; Workflow orchestration; Monitoring and maintenance; Security, cost and exam scenarios.

No. Check the awarding body’s current exam, experience and eligibility requirements. Exam booking and fees must be confirmed separately.

Contact London IT Training to discuss classroom, live online and corporate delivery arrangements.

Contact the training team for current duration, available dates, fees and software or lab requirements. Do not assume that licences, materials or examination fees are included.

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Talk to one of our advisers about dates, delivery options and certification routes. We’ll call you back within 1 working day.

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