Overview

Fundamentals of AWS AI and ML Solutions course is designed for cloud engineers, developers, and technical professionals who want to build a strong foundation in artificial intelligence (AI), machine learning (ML), and deep learning using AWS services. The course focuses on helping learners understand how machine learning systems work, how to identify the right ML approach for real-world problems, and how to use managed AWS AI/ML services to accelerate solution development. Learners will progress from core AI and ML fundamentals to hands-on exposure with Amazon SageMaker and a wide range of AWS AI services used for language, vision, speech, and intelligent automation use cases. The course comprises approximately 4–5 hours of video content, organised into three comprehensive modules, each divided into focused technical lessons. To reinforce learning, each module includes quizzes and in-video knowledge checks, allowing learners to validate both conceptual understanding and practical knowledge as they progress. - Module 1: Fundamentals of AI & ML - Module 2: Amazon Sagemaker - Module 3: AWS Managed AI Services By the end of the course, learners will be confident in understanding ML workflows, evaluating models, and choosing the right AWS AI services for business and technical requirements. - Understand how ML and deep learning models are operationalized in production systems. - Learn to prepare, manage, and operationalize ML data and features using SageMaker Data Wrangler, Feature Store, and Model Monitor. - Apply human-in-the-loop workflows to improve accuracy and reliability.

Syllabus

Fundamentals of AI & ML

Welcome to Week 1 of the Fundamentals of AWS AI and ML Solutions course. In this week, you will build a strong conceptual foundation in artificial intelligence and machine learning, starting with a clear understanding of what machine learning is and how it differs from artificial intelligence and deep learning.You will explore the types of data used in machine learning systems and examine the major categories of machine learning. This week also introduces you to AWS services for machine learning, providing an overview of how managed services from Amazon Web Services support model development, training, and deployment. As part of model evaluation, you will learn how to analyze classification results using confusion matrices, interpret their outcomes, and apply evaluation metrics for both classification and regression problems. The week concludes with an introduction to deep learning, followed by a discussion on how machine learning and deep learning models are used in production environments, including key considerations such as scalability, performance, and reliability.

Amazon Sagemaker

Welcome to Week 2. In this week, you will be introduced to Amazon SageMaker, AWS’s fully managed service for building, training, and deploying machine learning models at scale. You will begin with an overview of SageMaker and explore its core components through hands-on demonstrations. As the module progresses, you will take a deep dive into essential SageMaker capabilities such as Data Wrangler for data preparation, Feature Store for managing and reusing features, and Model Monitor for detecting data drift and maintaining model performance in production. By the end of this module, you will be able to confidently navigate the SageMaker ecosystem, prepare and manage ML data efficiently, operationalize features, monitor deployed models, and accelerate machine learning development using built-in templates and pretrained models.

AWS Managed AI Services

Welcome to Week 3 of the Fundamentals of AWS AI and ML Solutions course. In this module, you will begin by working with language-based AI services such as Amazon Comprehend, Amazon Translate, and Amazon Transcribe, learning how to extract insights from text, translate content across languages, and convert speech into text. The module then expands into speech and vision capabilities using Amazon Polly and Amazon Rekognition, enabling you to generate lifelike speech and analyze images and videos for faces, objects, and content moderation.You will also explore conversational and search-based AI solutions with Amazon Lex and Amazon Kendra. The module also covers personalization and document intelligence through Amazon Personalize and Amazon Textract, demonstrating how AWS AI services can be used to deliver tailored user experiences and extract structured data from scanned documents.