AWS Certified Machine Learning

Course Description

This training instills the skills to build, train, evaluate, and productionize machine learning (ML) models on the AWS platform. It covers AWS services and key machine learning concepts—from data preparation to model evaluation—along with optimization and integration of solutions in a cloud environment.

Learning Objectives

By the end of this course, participants will be able to:

• Master AWS services for Machine Learning: Learn to use services such as Amazon SageMaker, AWS Lambda, Rekognition, Polly, and Deep Learning AMIs.
• Prepare data for machine learning: Learn to clean, transform, and organize data for training ML models with tools such as AWS Glue and Amazon S3.
• Train and evaluate machine learning models: Choose the right algorithms, train models on SageMaker, and evaluate their performance using appropriate metrics.
• Optimize machine learning models: Apply techniques to improve model accuracy by tuning hyperparameters.
• Deploy ML models to production: Learn to deploy models in real time or batch mode via SageMaker.
• Monitor model performance in production: Use tools such as SageMaker Model Monitor to track and adjust models in production.
• Apply security and compliance best practices: Integrate security practices to protect data and ensure compliance with regulations using AWS services such as IAM and KMS.
• Prepare for the certification exam: Review key concepts and get ready for the AWS Certified Machine Learning – Specialty exam through simulations.

Who is this course for?

The course is aimed at a broad audience, including:

• Data Scientists: Professionals building ML models and using AWS for training, evaluation, and deployment.
• Machine Learning Engineers: Responsible for implementing, optimizing, and integrating ML models into production systems on AWS.
• Cloud solutions architects: Designers of architectures integrating machine learning solutions to solve specific business problems with AWS.
• Software developers: Those who integrate machine learning capabilities into their applications via AWS, using services such as SageMaker.
• Advanced data analysts: Professionals analyzing large datasets who want to deepen their machine learning skills on AWS.
• AI and machine learning consultants: Consultants who help companies design and deploy machine learning solutions on AWS.

Prerequisites

No specific prerequisites are required. This course is accessible to anyone wishing to discover AWS, but basic knowledge of IT or information systems can be an asset.

Course Syllabus

Introduction to Machine Learning on AWS and Data Preparation

• Introduction to AWS services for Machine Learning: Overview of AWS services dedicated to ML such as Amazon SageMaker, AWS Lambda, Amazon Rekognition, Amazon Polly, Deep Learning AMIs.
• Data preparation for machine learning with AWS Glue, Athena, and S3. Hands-on lab: Build a data preparation pipeline with AWS Glue and store the data in S3.

Training, Evaluation, and Optimization of Machine Learning Models

• Model training with SageMaker. Hands-on lab: Train a classification model with SageMaker.
• Model evaluation and optimization with metrics such as accuracy, recall, etc. Hands-on lab: Optimize a classification model in SageMaker.

Deploying Models to Production and Monitoring Performance

• Deploy models with SageMaker Endpoints and SageMaker Batch Transform. Hands-on lab: Deploy a real-time model with SageMaker Endpoints.
• Monitor model performance with SageMaker Model Monitor. Exam review: Summary of key concepts and exam simulation.

Course Highlights

• Teaching approach: Alternating theory and practice for better mastery of concepts.
• Qualified speakers: Specialized instructors with hands-on experience in Cloud.
• Tools and learning materials: Access to online resources, live demonstrations, and real case studies.
• Accessibility: Training open to all, with no advanced technical prerequisites.

Teaching Methods and Tools Used

• Live demonstrations with AWS cloud services.
• Hands-on workshops and real case studies in various sectors (industry, retail, healthcare).
• Feedback: Sharing best practices and common pitfalls in companies.
• Simulations and tools: Use of simulators and AWS for interactive workshops.

Assessment

• Multiple-choice quiz at the end of the course.
• Practical case studies.
• Continuous assessment with personalized feedback.

Normative 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 (Service Organization Control).
• PCI-DSS (Payment Card Industry Data Security Standard).
• NIST Cybersecurity Framework.