Generative AI for AWS Cloud Engineers

Instructor: Whizlabs Instructor

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2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Evaluate and optimize Foundation Model performance using RAG and Guardrails.
Evaluate key principles and legal risks of Responsible AI.
Implement security and privacy controls within the AWS Shared Responsibility Model.

Skills you'll gain

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There are 2 modules in this course

Generative AI for AWS Cloud Engineers course is designed for technical professionals aiming to master the integration, deployment, and optimization of large language models (LLMs) within the AWS cloud infrastructure. It aims to bridge the gap between traditional cloud engineering and the rapidly evolving landscape of generative AI.

This course facilitates learners with approximately 4:00–5:00 Hours of Video lectures that provide a deep dive into both architectural theory and hands-on implementation. The course is structured into 3 Comprehensive Modules, with each module further split into technical lessons. To test the practical understanding of learners, every module includes Quizzes, and In-Video Knowledge Checks. Enroll in our “Generative AI for AWS Cloud Engineers” course to lead the next generation of cloud-native AI innovation. - Module 1: Gen AI on AWS - Amazon Bedrock - Module 2: Security and Governance of AI Solutions This course is specifically designed for technical professionals and cloud practitioners who aim to bridge the gap between core infrastructure and modern AI implementation. By the end of this course, a learner will be able to: - Understand foundational concepts, methods, and strategies related to artificial intelligence (AI), machine learning (ML), and generative AI (Gen AI) within the AWS ecosystem. - Identify appropriate AI/ML and generative AI technologies to address specific organizational use cases effectively. - Apply responsible practices in the utilization of AI, ML, and Generative AI technologies.

Welcome to Week 1 of the Generative AI for AWS Cloud Engineers course. This week, we will explore the Amazon Bedrock, starting with a comprehensive overview and hands-on demos to understand how to leverage FMs within the AWS ecosystem. You will learn how to customize models through Fine-tuning and implement advanced architectures like Retrieval-Augmented Generation (RAG) to enhance model accuracy with external data. By the end of this module, you will be able to configure Guardrails to ensure responsible AI usage, orchestrate complex tasks with Amazon Bedrock Agents, and navigate the Pricing structures to build cost-effective Generative AI solutions.

What's included

14 videos2 readings2 assignments

14 videos Total 100 minutes

Types of Foundation Models 5 minutes
Business Metrics for Generative AI 8 minutes
Amazon Bedrock - Overview 9 minutes
Amazon Bedrock - Demo 6 minutes
Foundation Models on Amazon Bedrock 6 minutes
Finetuning Foundation Models - Demo 7 minutes
Evaluation Metrics of Foundation models 9 minutes
Understanding RAG Architecture of LLM 6 minutes
AWS Services for Storage of Vector Embeddings 9 minutes
Amazon Bedrock RAG & Knowledge Base - Demo 9 minutes
Amazon Bedrock - GuardRails 6 minutes
Amazon Bedrock - GuardRails - Demo 14 minutes
Amazon Bedrock Agents 4 minutes
Amazon Bedrock - Pricing 5 minutes

2 readings Total 25 minutes

Welcome to the Course 15 minutes
Overview of Gen AI on AWS - Amazon Bedrock 10 minutes

2 assignments Total 80 minutes

Amazon Bedrock and its features - Knowledge Check 35 minutes
Gen AI on AWS - Amazon Bedrock - Assessment 45 minutes

Welcome to Week 2. This week, we will explore the foundational principles and ethical practices required to build and deploy Responsible AI systems. We will learn how to select models using responsible criteria and navigate the complex legal risks associated with generative AI. Furthermore, we will dive into the AWS Shared Responsibility Model as it applies to AI, identifying specific AWS tools and services designed to secure AI systems and ensure robust governance. By the end of this week, you will be able to implement security and privacy considerations effectively to manage and protect your AI applications within the AWS ecosystem.

What's included

10 videos2 readings3 assignments

10 videos Total 42 minutes

Key Principles of Responsible AI 4 minutes
Responsible practices to select a model 5 minutes
Legal risks of working with generative AI 6 minutes
Best Practices for Data Collection 4 minutes
Data privacy and the importance of data consent. 4 minutes
AWS Tools for Responsible AI 3 minutes
AWS Services for Securing AI Systems 5 minutes
AWS Shared Responsibility Model 4 minutes
Securiy and Privacy consideration 4 minutes
AWS Services for Governance of AI Applications 4 minutes

2 readings Total 25 minutes

Overview of Security and Governance of AI Solutions 10 minutes
What's Next? 15 minutes

3 assignments Total 130 minutes

Responsible AI - Knowledge Check 25 minutes
Project: Creating Guardrails in Amazon Bedrock 80 minutes
Security and Governance of AI Solutions - Assessment 25 minutes

Instructor

Whizlabs Instructor

Whizlabs

140 Courses 104,870 learners

Offered by

Whizlabs

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