Generative AI & AWS AI Practitioner Certification

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Overview

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This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This comprehensive course covers the essentials of Generative AI and prepares you for the AWS AI certification exam. You’ll start by exploring AI/ML fundamentals, including various machine learning models, data types, and the differences between supervised, unsupervised, and reinforcement learning. As you advance, the course dives into Generative AI, focusing on foundation models, Large Language Models (LLM), and transformer architectures that power modern AI systems. You will also gain hands-on experience with AWS tools like Amazon Bedrock and SageMaker, learning to deploy, fine-tune, and optimize models in a cloud environment. The course equips you with both theoretical knowledge and practical skills, ensuring you're prepared for real-world applications. Throughout the journey, you’ll first build a strong foundation in AI/ML concepts and deep learning. From there, you'll dive into the exciting world of Generative AI, learning how it generates creative outputs and its applications across industries. You'll also explore AWS’s generative AI tools like Amazon Bedrock and SageMaker, which will help you master the skills needed to work in the cloud and deploy scalable AI models. By the end of the course, you’ll have a deep understanding of AI and its applications, making you ready to tackle complex problems with AWS's powerful tools. This course is designed for anyone interested in pursuing a career in AI and cloud computing, from aspiring data scientists to IT professionals looking to enhance their AI knowledge. There are no formal prerequisites, but familiarity with basic programming concepts or cloud computing can be beneficial. The course is suitable for intermediate learners with some foundational knowledge in tech or AI. By the end of the course, you will be able to develop generative AI models, fine-tune them for specific use cases, integrate them with AWS tools, and deploy AI applications on the cloud. You will also be well-prepared for the AWS AI certification exam, demonstrating your expertise in this emerging field.

Syllabus

Introduction

In this module, we will introduce the course objectives and outline how it will guide you through the learning process of Generative AI. You will also be provided with an overview of the AWS AI certification exam, including key topics and resources. By the end of this section, you'll have a clear understanding of the course structure and your learning path ahead.

AI ML Fundamentals

In this module, we will explore the foundational concepts of Artificial Intelligence and Machine Learning. You will learn about key topics such as recommendation systems, machine learning models, and the different types of learning methods. By the end of this section, you'll have a comprehensive understanding of AI and ML fundamentals, including the differences between supervised, unsupervised, and reinforcement learning, data types, inference techniques, and deep learning applications.

Generative AI Fundamentals

In this module, we will dive into the fundamentals of Generative AI, covering key concepts like foundation models and Large Language Models (LLMs). You will also learn about the transformer architecture, which is pivotal for modern AI models such as GPT and BERT. Additionally, we will explore how Generative AI can generate human-like text, with applications in chatbots, content creation, and more.

Generative AI at AWS

In this module, we will explore Amazon’s suite of tools and services for implementing Generative AI, with a focus on Amazon Bedrock. You will learn how to deploy and customize foundation models, apply prompt engineering, and integrate AI models with knowledge bases and other AWS services. Additionally, we will cover advanced topics like Retrieval Augmented Generation (RAG), the role of agents in automation, pricing considerations, and safety measures like guardrails. By the end of this section, you will be equipped to build and scale generative AI applications using AWS.

Fine-Tuning Your Model

In this module, we will focus on the techniques used to fine-tune AI models for better performance on specific tasks or datasets. You will compare fine-tuning with continued pre-training to understand their distinct purposes in machine learning. Additionally, we will guide you through the process of creating custom models in Amazon Bedrock, allowing you to tailor AI solutions for specialized applications.

Build Your Own Model

In this module, we will guide you through the end-to-end process of building your own AI model, from initial concept to deployment. You will learn how to prepare data, select algorithms, train models, and deploy them using Amazon SageMaker. We will also cover the roles in an ML team, MLOps best practices, and provide a hands-on demonstration of building, optimizing, and deploying a machine learning model in a real-world environment. By the end of this section, you will have the knowledge and tools to create, train, and deploy custom AI models for your applications.

Monitoring Your Model

In this module, we will focus on monitoring the performance of your AI models through both business and technical metrics. You will learn how to track business metrics that gauge the success and value of your models in a business context. Additionally, we will explore essential technical metrics for monitoring the efficiency and effectiveness of machine learning models, ensuring they operate at their best.

Responsible AI

In this module, we will dive into the ethical considerations that must guide the development of AI systems. You will explore key topics such as fairness, transparency, and accountability, ensuring AI technologies are responsible and equitable. We will also discuss strategies for tackling AI challenges like bias and explainability, providing you with the tools to address ethical dilemmas in AI development and deployment.

AWS AI ML Services

In this module, we will introduce you to the comprehensive AI and ML services offered by AWS. You will learn how to utilize services such as Amazon Comprehend for text analysis, Amazon Lex for building conversational interfaces, and Amazon Rekognition for image and video analysis. We’ll also cover tools like Amazon Personalize for product recommendations and Amazon Polly for text-to-speech capabilities. By the end of this section, you will be equipped to enhance your AI projects with AWS’s wide array of specialized services.

Getting Ready for Exam

In this module, we will provide you with essential tips and strategies to prepare for the AWS AI certification exam. You will receive guidance on the key topics to focus on and how to structure your study sessions for optimal results. By the end of this section, you'll feel confident and fully equipped to succeed in the exam.