What you'll learn

• Ingest, transform and prepare data for ML using AWS Glue, S3 and SageMaker Feature Store.
• Train, evaluate and tune ML models with SageMaker Training Jobs, Experiments and Debugger.
• Deploy models at scale using SageMaker endpoints, batch transforms and A/B testing.
• Automate ML workflows with SageMaker Pipelines and Step Functions.
• Monitor model performance, detect drift and maintain quality with SageMaker Model Monitor.

Who should attend?

This training is designed for ML engineers, data scientists and cloud architects who build and operate ML systems in production. It is also suitable for DevOps engineers moving into MLOps roles and software engineers adding ML capabilities to existing platforms.

Prerequisites

• AWS Certified AI Practitioner or equivalent cloud experience.
• Basic Python programming and familiarity with ML concepts.
• No advanced mathematics required — focus is on engineering and operations.

Course curriculum

Module 1 – ML architecture on AWS

Overview of the AWS ML stack, SageMaker core components, choosing the right service for data, training and inference workloads.

Module 2 – Data preparation & feature engineering

Building data pipelines with Glue and S3, feature engineering and versioning with SageMaker Feature Store, data quality and governance.

Module 3 – Model training & evaluation

Configuring SageMaker Training Jobs, distributed training strategies, hyperparameter tuning with Automatic Model Tuning, metrics and evaluation best practices.

Module 4 – Model deployment & serving

Real-time endpoints, serverless inference, batch transforms, shadow deployments and A/B testing strategies for production rollouts.

Module 5 – MLOps & automation

SageMaker Pipelines, Step Functions and CI/CD for ML, automated retraining triggers, infrastructure as code for ML workloads.

Module 6 – Monitoring & exam readiness

SageMaker Model Monitor for drift detection, cost optimisation, responsible AI in production, and MLA-C01 exam preparation strategies.

Certification exam

• Exam: AWS Certified Machine Learning Engineer – Associate (MLA-C01).
• Format: Multiple-choice and multiple-response questions.
• Duration: 130 minutes.
• Language: English.
• Credential: Official AWS Associate-level certification.

Why this certification matters

• Validate production ML engineering skills with an official AWS credential.
• Stand out in MLOps and cloud-native AI engineering roles.
• Bridge the gap between experimentation and scalable, reliable ML systems.
• Prepare for senior ML architect and principal engineer pathways.

What's next?

After this certification, learners typically progress to the AWS Certified Solutions Architect – Professional, advanced MLOps architecture roles, or Generative AI solution design on AWS using Amazon Bedrock and SageMaker.