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AI/ML & Advanced AWS Services

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AI/ML & Advanced AWS Services

Instructeur : Whizlabs Instructor

Inclus avec

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Débutant

Expérience recommandée

6 heures à compléter

Planning flexible

Apprenez à votre propre rythme

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Débutant

Expérience recommandée

6 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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

Compétences que vous acquerrez

Catégorie : Retrieval-Augmented Generation
Catégorie : Artificial Intelligence and Machine Learning (AI/ML)
Catégorie : Natural Language Processing
Catégorie : MLOps (Machine Learning Operations)
Catégorie : Generative AI Agents
Catégorie : Artificial Intelligence
Catégorie : Image Analysis
Catégorie : Computer Vision
Catégorie : Large Language Modeling
Catégorie : Model Training
Catégorie : Data Wrangling

Outils que vous découvrirez

Catégorie : Generative AI
Catégorie : Agentic Workflows
Catégorie : Amazon Web Services
Catégorie : Prompt Engineering
Catégorie : AI Workflows
Catégorie : Model Deployment
Catégorie : Amazon Bedrock
Catégorie : AWS SageMaker

Détails à connaître

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mai 2026

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6 devoirs

Enseigné en Anglais

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Il y a 3 modules dans ce cours

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.

Détails du module

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.

Inclus

9 vidéos2 lectures2 devoirs1 sujet de discussion

9 vidéosTotal 63 minutes

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

2 lecturesTotal 10 minutes

Welcome to the Course5 minutes
Overview of Advanced GenAI Techniques5 minutes

2 devoirsTotal 60 minutes

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

1 sujet de discussionTotal 5 minutes

Meet & Greet5 minutes

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.

Inclus

11 vidéos1 lecture2 devoirs

11 vidéosTotal 46 minutes

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

1 lectureTotal 5 minutes

Overview of AWS AI Services5 minutes

2 devoirsTotal 60 minutes

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

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.