Learning objectives

At the end of this training, participants will be able to:

• Master AWS services dedicated to machine learning, including Amazon SageMaker, AWS Lambda, Amazon Rekognition, Amazon Polly and Deep Learning AMIs.
• Prepare and transform datasets for ML workflows using AWS Glue, Amazon Athena and Amazon S3.
• Train, evaluate and tune ML models in SageMaker by selecting the right algorithms and metrics.
• Optimise model accuracy through hyperparameter tuning, experiment tracking and automation.
• Deploy ML models into real-time endpoints or batch pipelines on SageMaker.
• Monitor and troubleshoot models in production with SageMaker Model Monitor and observability tooling.
• Apply AWS security, governance and compliance practices with IAM, KMS and encryption policies.
• Prepare efficiently for the AWS Certified Machine Learning exam with targeted reviews and simulations.

Who this programme is for

The training is aimed at a wide audience, including:

• Data Scientists building ML models who want to industrialise their workflows on AWS.
• Machine Learning Engineers responsible for optimisation, deployment and monitoring of ML systems.
• Cloud Solutions Architects designing AWS architectures that embed intelligent services.
• Software Developers integrating ML capabilities into applications via SageMaker endpoints.
• Advanced Data Analysts seeking to deepen ML expertise while scaling workloads in the cloud.
• AI and ML Consultants supporting enterprises in designing and deploying AWS-based ML solutions.

Prerequisites

No specific prerequisites are required. The course is open to anyone keen to explore AWS. A basic understanding of computer science or information systems is a helpful asset.

Detailed programme

Introduction to machine learning on AWS and data preparation

• Overview of AWS ML services such as Amazon SageMaker, AWS Lambda, Amazon Rekognition, Amazon Polly and Deep Learning AMIs.
• Data preparation workflows with AWS Glue, Amazon Athena and Amazon S3, including a practical pipeline workshop.

Training, evaluation and optimisation of ML models

• Model training with SageMaker using built-in algorithms or custom containers, plus a guided classification lab.
• Assessment & certification, optimisation and hyperparameter tuning with the right metrics and automation features, followed by a practical optimisation exercise.

Deployment and performance monitoring

• Real-time and batch deployment patterns using SageMaker Endpoints and SageMaker Batch Transform, with an end-to-end deployment workshop.
• Production monitoring through SageMaker Model Monitor, alerting, drift detection and an exam review that consolidates every concept.

Key takeaways & deliverables

• Balanced pedagogy with alternating theory, labs and debriefs for faster assimilation.
• Expert trainers who implement AWS ML workloads every day.
• Premium learning tools: live demos, AWS console labs and real customer case studies.
• Accessible format with support for individual learners and corporate cohorts.

Teaching approach

• Live demonstrations inside the AWS cloud environment.
• Hands-on workshops and sector-specific case studies.
• Feedback sessions highlighting best practices and pitfalls observed in the field.
• Simulations and AWS tools to rehearse exam scenarios and production constraints.

Assessment & certification

• MCQ at the end of training.
• Practical case studies.
• Continuous evaluation with personalized feedback.

Standards & references

• AWS Well-Architected Framework.
• GDPR (General Data Protection Regulation).
• CCPA (California Consumer Privacy Act).
• HIPAA (Health Insurance Portability and Accountability Act).
• ISO 27001 & SOC 2.
• PCI-DSS (Payment Card Industry Data Security Standard).
• NIST Cybersecurity Framework.