AWS Certified AI Practitioner

Overview

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Welcome to the transformative journey that is the AWS Certified AI Practitioner Course! In today's rapidly changing AI landscape, having a firm grasp of AI concepts is critical, but knowing how to implement these concepts on AWS is where the challenge—and opportunity—lies. If you've ever felt overwhelmed by the complexities of integrating AI into AWS, you're not alone. Each tutorial can seem straightforward, only to reveal its true difficulty when you're down in the weeds, applying AI to your AWS solutions. This course is crafted to address just that. Designed for those who already possess a foundational understanding of AWS, we focus on bridging the gap between theoretical knowledge and real-world AWS applications. Through practical, scenario-based learning, you'll gain the skills to navigate and excel in the AWS AI ecosystem, advancing beyond the basics with valuable, applicable insights. Additionally, this course will prepare you to confidently appear for the AWS Certified AI Practitioner exam, equipping you with the knowledge and skills to achieve this credential and validate your expertise in AI-powered AWS solutions. Course Modules 1. Fundamentals of AI and ML Delve into essential AI concepts, understanding the distinctions between AI, machine learning, and deep learning. You'll engage with various data types, learning methods, and identify practical AI and ML use cases, laying a robust foundation for your AI endeavors on AWS. 2. Fundamentals of Generative AI Focus on the unique attributes of generative AI, including tokens, embeddings, and foundation models' lifecycle. Discuss cost considerations and AWS infrastructure specific to generative AI, alongside real-world applications, advantages, and constraints. 3. Applications of Foundation Models Learn about designing and customizing applications using foundation models. From selecting and fine-tuning pre-trained models to implementing retrieval-augmented generation and vector databases, gain insights into effective AI model deployment on AWS. Explore best practices in prompt engineering and metrics for evaluating model performance. 4. Guidelines for Responsible AI Explore foundational principles and tools for creating responsible AI applications. Discuss responsible model selection, legal risk management, and bias mitigation, ensuring your AI solutions are both safe and ethical, grounded in transparent, human-centered design. 5. Security, Compliance, and Governance for AI Solutions Address key aspects of securing AI systems on AWS, from best practices in data engineering to regulatory compliance and governance strategies, ensuring your AI applications are secure, compliant, and trustworthy. 6. Conclusion and Next Steps Summarize key concepts, complete a final assessment, and explore resources for ongoing learning in the dynamic AWS AI/ML space. Reflect on AI's future impact within AWS and beyond, preparing you for continued advancement in this exciting field. Equip yourself with the skills to master AI on AWS through this highly practical, hands-on course, where theory meets the complexity of real-world application. Whether you're looking to enhance your current role or forge new paths in AI, this course is your launchpad into the future of AI on AWS.

Syllabus

Course Introduction

This introductory module provides an overview of the AWS Certified AI Practitioner course, outlining its objectives, certification benefits, and exam structure. You’ll assess your prerequisites, explore the certification process, and set up your own AWS account. By the end of this module, you’ll have a clear understanding of what to expect and how to prepare for the journey ahead.

Fundamentals of AI and ML

This module lays the groundwork for understanding artificial intelligence and machine learning, focusing on their core concepts and real-world applications. You’ll explore different learning types, inference methods, and AI data structures. Additionally, you’ll get an introduction to MLOps and the machine learning development lifecycle, setting the stage for AI implementation on AWS.

Fundamentals of Generative AI

This module introduces the core concepts of generative AI, including tokens, chunking, and embeddings. You’ll explore the lifecycle of foundation models, their capabilities, and limitations in real-world applications. Additionally, you’ll gain insights into the AWS infrastructure for building generative AI solutions and the cost considerations for optimizing performance, availability, and redundancy.

Applications of Foundation Models

This module explores how to design, customize, and optimize foundation model applications on AWS. You'll learn how to select pre-trained models, adjust inference parameters, and leverage retrieval-augmented generation (RAG) with vector databases. Additionally, the module covers multi-step task agents, prompt engineering techniques, fine-tuning processes, and performance evaluation to ensure efficient deployment of foundation models.

Guidelines for Responsible AI

This module focuses on the ethical and practical considerations for developing responsible AI applications. You’ll explore key features of responsible AI, tools for identifying responsible practices, and strategies for mitigating bias in datasets. Additionally, the module covers legal risks in generative AI, transparent and explainable models, and human-centered design principles to ensure ethical and effective AI deployment.

Security, Compliance, and Governance for AI Solutions

In this module, you’ll learn how to secure AI systems using AWS services and follow best practices for secure data engineering. You’ll explore the importance of source citation, data lineage, and privacy considerations in AI systems. The module also covers regulatory compliance standards and governance strategies for AI, equipping you with the knowledge to ensure that your AI solutions meet legal, ethical, and security requirements.

Conclusion and Next Steps

This final module provides a comprehensive wrap-up of the course, including a final assessment to evaluate your progress. You’ll also explore next steps in your certification journey, along with valuable resources for continual learning in the AWS AI/ML space. The module concludes with a discussion on the future impact of AI within AWS and beyond, empowering you to keep advancing your AI expertise.