Whizlabs

AI/ML & Advanced AWS Services

Whizlabs

AI/ML & Advanced AWS Services

This course is part of AWS Core+: Technical Essentials for Team Managers Specialization

Whizlabs Instructor

Instructor: Whizlabs Instructor

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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • 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

Details to know

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Recently updated!

May 2026

Assessments

6 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the AWS Core+: Technical Essentials for Team Managers Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 3 modules in this course

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.

What's included

9 videos2 readings2 assignments1 discussion prompt

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.

What's included

11 videos1 reading2 assignments

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.

What's included

8 videos2 readings2 assignments

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