AI/ML & Advanced AWS Services

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AI/ML & Advanced AWS Services

位教师：Whizlabs Instructor

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3个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

6 小时完成

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

6 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Understand advanced Generative AI concepts, prompt engineering, foundation models, and RAG architectures on AWS
Learn machine learning and MLOps workflows using Amazon SageMaker and AWS AI/ML operational services
Explore AWS AI services for conversational AI, intelligent search, speech, vision, translation, and personalization use cases

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该课程共有3个模块

The AI/ML & Advanced AWS Services course provides foundational and intermediate knowledge of Generative AI, AWS AI services, machine learning workflows, and MLOps practices used to build intelligent cloud applications. Learners will explore advanced Generative AI concepts, AWS AI/ML services, foundation models, prompt engineering, intelligent search, conversational AI, computer vision, and machine learning operations on AWS.

The course covers advanced Generative AI techniques including prompt engineering, fine-tuning, RAG architecture, foundation models, Amazon Bedrock, Guardrails, Bedrock Agents, and AI-powered application workflows. Learners will also explore AWS AI services such as Amazon Rekognition, Amazon Lex, Amazon Kendra, Amazon Polly, Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Textract, Amazon Personalize, and other intelligent AWS services. In addition, the course introduces machine learning and MLOps concepts using Amazon SageMaker, SageMaker Feature Store, SageMaker Data Wrangler, SageMaker Model Monitor, SageMaker JumpStart, and AWS MLOps services to help learners understand end-to-end ML lifecycle management and operational AI workflows. This course is structured into three modules with approximately 7–9 hours of video content and quizzes to reinforce learning. Course Modules: Module 1: Advanced GenAI Techniques Module 2: AWS AI Services Module 3: Machine Learning & MLOps By the end of this course, learners will be able to: Understand advanced Generative AI concepts and foundation models Explore prompt engineering, fine-tuning, and RAG architectures Understand Amazon Bedrock, Guardrails, Agents, and AI integrations Explore AWS AI services for speech, vision, search, translation, and conversational AI Understand machine learning workflows using Amazon SageMaker Explore MLOps concepts, monitoring, feature stores, and ML lifecycle management Identify appropriate AWS AI/ML services for different business and application requirements This course is ideal for learners preparing for AWS AI/ML roles, Generative AI solutions, machine learning operations, cloud AI engineering, and AWS AI certification fundamentals.

Welcome to the Advanced GenAI Techniques module , you’ll focus on advanced generative AI techniques used to build scalable and controlled AI applications on AWS. We’ll begin with Understanding RAG Architecture of LLM and AWS Services for Storage of Vector Embeddings, helping you understand how external knowledge is integrated into AI models for more accurate and context-aware responses.Next, you’ll explore hands-on implementation with Amazon Bedrock RAG & Knowledge Base - Demo, followed by Amazon Bedrock Guardrails and its demo, enabling you to enforce safety, compliance, and control over model outputs.As the week progresses, you’ll dive into Amazon Bedrock Agents and integrations with services like CloudWatch and S3, along with PartyRock - Amazon Bedrock Playground to experiment with generative AI use cases. You’ll also review Amazon Bedrock Pricing to understand cost considerations.By the end of this week, you’ll have a strong understanding of advanced GenAI techniques and be able to design, secure, and evaluate AI-powered applications using Amazon Bedrock.

涵盖的内容

9个视频2篇阅读材料2个作业1个讨论话题

9个视频总计63分钟

Understanding RAG Architecture of LLM6分钟
AWS Services for Storage of Vector Embeddings9分钟
Amazon Bedrock RAG & Knowledge Base - Demo11分钟
Amazon Bedrock - GuardRails6分钟
Amazon Bedrock - GuardRails - Demo14分钟
Amazon Bedrock Agents4分钟
Amazon Bedrock Integrations - Cloudwatch - S34分钟
PartyRock - Amazon Bedrock Playground5分钟
Amazon Bedrock - Pricing5分钟

2篇阅读材料总计10分钟

Welcome to the Course5分钟
Overview of Advanced GenAI Techniques5分钟

2个作业总计60分钟

Advanced GenAI Techniques - Assessment30分钟
Building Advanced GenAI Applications on AWS - Knowledge Check30分钟

1个讨论话题总计5分钟

Meet & Greet5分钟

Welcome to the AWS AI Services module, you’ll focus on AWS AI services that enable you to add intelligent capabilities to your applications. We’ll begin with Amazon Comprehend and Amazon Translate, along with demos, to understand how to process and analyze text using natural language processing. Next, you’ll explore speech and voice services such as Amazon Transcribe and Amazon Polly, helping you convert speech to text and text to speech for real-world use cases. As the week progresses, you’ll dive into computer vision and conversational AI with Amazon Rekognition and Amazon Lex, along with demos to understand image analysis and chatbot development. You’ll also explore advanced services like Amazon Kendra for intelligent search, Amazon Textract for document processing, Amazon Personalize for recommendations, and Amazon Mechanical Turk and Amazon Augmented AI (A2I) for human-in-the-loop workflows. By the end of this week, you’ll be able to leverage AWS AI services to build applications with capabilities such as NLP, speech recognition, vision processing, and intelligent automation.

涵盖的内容

11个视频1篇阅读材料2个作业

11个视频总计46分钟

Amazon Comprehend5分钟
Amazon Translate3分钟
Amazon Transcribe 3分钟
Amazon Polly4分钟
Amazon Rekognition4分钟
Amazon Lex6分钟
Amazon Kendra5分钟
Amazon Mechanical Turk3分钟
Amazon Augmented AI (A2I)4分钟
Amazon Personalize4分钟
Amazon Textract4分钟

1篇阅读材料总计5分钟

Overview of AWS AI Services5分钟

2个作业总计60分钟

AWS AI Services - Assessment30分钟
Applied AI Services on AWS - Knowledge Check30分钟

Welcome to the Machine Learning & MLOps module, you’ll focus on machine learning workflows and MLOps practices using AWS. We’ll begin with an Introduction to Amazon SageMaker and a hands-on SageMaker Demo, helping you understand how to build, train, and deploy machine learning models at scale. Next, you’ll explore key SageMaker capabilities, including Data Wrangler for data preparation, Feature Store for managing reusable features, and Model Monitor for tracking model performance and detecting data drift. As the week progresses, you’ll learn how to accelerate development using SageMaker JumpStart, followed by an introduction to MLOps and the AWS Services for MLOps, enabling you to automate, monitor, and manage the ML lifecycle efficiently. By the end of this week, you’ll have a solid understanding of ML workflows and be equipped to implement MLOps practices for building and maintaining scalable machine learning solutions on AWS.

涵盖的内容

8个视频2篇阅读材料2个作业

8个视频总计52分钟

Introduction to Amazon Sagemaker4分钟
Amazon Sagemaker - Demo11分钟
Amazon Sagemaker Data Wrangler - Deep Dive7分钟
Amazon Sagemaker Feature Store - Deep Dive8分钟
Amazon Sagemaker Model Monitor - Deep Dive9分钟
Amazon Sagemaker Jumpstart5分钟
What is MLOps ?5分钟
AWS Services for MLOps4分钟

2篇阅读材料总计10分钟

Overview of Machine Learning & MLOps5分钟
Course Conclusion5分钟

2个作业总计60分钟

Machine Learning & MLOps - Assessment30分钟
ML Workflows & Operational Excellence on AWS - Knowledge Check30分钟

位教师

Whizlabs Instructor

Whizlabs

159 门课程121,765 名学生

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Whizlabs

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