Build RAG on AWS Cloud Training in Sao Paulo
+1,200 Enrolled
Some of the highlights of this course.
60+ Hours Course Duration
20 Practical Labs Hands-On Labs
1 End-to-End RAG Application Capstone Project
7+ Core Services AWS Services Covered
20 Modules Modules Included
Industry-Recognized Completion Certificate Certification
Gain practical experience in building enterprise-grade Retrieval-Augmented Generation (RAG) applications on AWS through 60+ hours of instructor-led training, 20 hands-on labs, real-world projects, and production-ready cloud deployment practices.
Tuition Fee and Training Options
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We are currently preparing new batches for this course. Please check back later or contact us for more information.
Course Description
Build RAG on AWS Cloud Overview
what will you get
Key Features & Highlights
End-to-End RAG Implementation
Build complete Retrieval-Augmented Generation pipelines from document ingestion to intelligent response generation using AWS managed services.
Amazon Bedrock Integration
Learn to integrate foundation models and embedding generation using Amazon Bedrock for scalable Generative AI applications.
Vector Search with OpenSearch
Implement vector indexing and semantic retrieval using Amazon OpenSearch Service for enterprise-grade knowledge retrieval.
Serverless API Deployment
Deploy secure and scalable RAG APIs using AWS Lambda and API Gateway for production-ready AI applications.
This course provides practical, hands-on training to design, build, secure, and deploy enterprise-grade Retrieval-Augmented Generation applications on AWS using production-ready cloud architecture and best practices.
Why do Build RAG on AWS Cloud at Nevolearn
Learn like never before. Not just learning, you interact and gain real experience.
- Get practical, real-world learning experience
- Engage in interactive sessions and activities
- Apply concepts through hands-on exercises
Build skills that matter. Go beyond theory and develop job-ready expertise.
- Track and measure your skill progress
- Identify strengths and areas for improvement
- Gain industry-relevant knowledge and tools
Achieve your career goals with structured learning and expert guidance.
- Learn from industry experts and mentors
- Prepare for certifications and real-world challenges
- Boost your career growth with in-demand skills
Build RAG on AWS Cloud Curriculum
Concepts
● What is Generative AI vs RAG (simple explanation)
● Why RAG is used in real jobs (support bots, search assistants, knowledge chat)
● RAG flow: Documents → Embeddings → Vector DB → Retrieve → LLM Answer
● “Hallucination” and how retrieval reduces it
Lab 1
● Explore a simple prebuilt RAG demo (provided)
● Identify each RAG component in the demo (documents, retrieval, response)
● Write a basic “RAG checklist” for your own project
Concepts
● AWS Regions, Availability Zones, basic pricing idea
● IAM basics: users, roles, policies (beginner friendly)
● Why managed services matter for freshers
Lab 2
● Create AWS account setup checklist (trainer-guided)
● Create an IAM user/role with minimum permissions (guided policy)
● Enable CloudWatch logging basics
Concepts
● What is object storage, buckets, folders
● Naming, versioning, encryption basics
● How S3 fits into a RAG document pipeline
Lab 3
● Create an S3 bucket for a “knowledge base”
● Upload PDFs/text files
● Enable versioning + basic encryption
● Set correct access permissions (avoid public access)
Concepts
● Why clean text matters for search accuracy
● Common document issues: headers, page numbers, repeated text
● Simple preprocessing steps (no heavy coding)
Lab 4
● Use a guided extraction approach (trainer-provided script / managed flow)
● Convert sample files into clean text format
● Store cleaned output back in S3 (separate folder structure)
Concepts
● What is chunking and why it matters
● Chunk size, overlap (simple rules)
● Metadata: file name, section, page number
Lab 5
● Apply chunking to extracted text using a guided workflow
● Generate chunk files + metadata JSON
● Save chunks to S3 for embedding step
About Build RAG on AWS Cloud Certification
Follow these simple steps to earn your professional certification and validate your expertise.
Enroll in the AWS RAG Training program.
Complete all 20 training modules and practical labs.
Participate in hands-on implementation exercises using AWS services.
Build and deploy an end-to-end RAG application as a capstone project.
Complete project validation and technical assessment.
Prerequisites
Basic understanding of cloud computing concepts, APIs, and web applications is recommended. Familiarity with AWS services and basic programming knowledge will be helpful for hands-on labs and project implementation. An active Amazon Web Services account is required for practical exercises.
Who should attend the Build RAG on AWS Cloud training
This course is ideal for professionals and learners who want to build scalable Generative AI applications and deploy Retrieval-Augmented Generation architectures on AWS Cloud. It is suitable for Generative AI Developers, AWS Developers, Cloud Engineers, DevOps Engineers, AI/ML Engineers, Data Engineers, Software Developers, Technical Architects, IT professionals transitioning into AI, and final-year engineering students looking to build practical cloud AI skills.
COMMON QUESTIONS
Build RAG on AWS Cloud FAQs
This course is suitable for developers, cloud engineers, AI/ML engineers, DevOps professionals, and students interested in Generative AI on AWS.
No. Basic cloud knowledge is helpful, but prior AWS experience is not mandatory.
Basic programming knowledge is recommended for hands-on labs.
Yes. The course starts with foundational concepts and gradually moves into advanced RAG implementation.
Yes, an active Amazon Web Services account is required for labs and project deployment.
Yes. Final-year students and entry-level professionals can join and build practical cloud AI skills.
Yes, but technical familiarity with APIs and cloud basics will help.
Basic understanding of cloud computing, APIs, and application workflows.
No, there is no entrance test for enrollment.
Yes, trainer-guided support is provided during practical sessions.
Skills Covered
What Will You Learn?
Soaring Demand and Accelerated Growth
AWS Generative AI Engineer
Annual Salary
Workers/SalaryHiring Companies
Design, build, and deploy enterprise-grade Retrieval-Augmented Generation (RAG) applications on AWS using managed services like Amazon Bedrock, OpenSearch, Lambda, and S3. Responsibilities include implementing vector search pipelines, integrating LLMs, optimizing cloud AI workloads, managing API deployments, securing AI infrastructure with IAM, and monitoring performance using CloudWatch.
FOR AWS Generative AI Engineer
Annual Salary
Workers/SalaryHiring Companies
Design, build, and deploy enterprise-grade Retrieval-Augmented Generation (RAG) applications on AWS using managed services like Amazon Bedrock, OpenSearch, Lambda, and S3. Responsibilities include implementing vector search pipelines, integrating LLMs, optimizing cloud AI workloads, managing API deployments, securing AI infrastructure with IAM, and monitoring performance using CloudWatch.
for AWS Generative AI Engineer
AWS Generative AI Engineers are responsible for building scalable AI applications powered by cloud-native architecture. They develop production-ready RAG systems, improve knowledge retrieval accuracy, optimize infrastructure costs, and ensure secure deployment of Generative AI applications for enterprise use cases.
Set your teams up with this course
This course equips learners with practical expertise to design, build, and deploy enterprise-grade Retrieval-Augmented Generation (RAG) applications on AWS Cloud. By working with managed services like Amazon Bedrock, OpenSearch, Lambda, and S3, participants gain real-world cloud AI implementation skills, strengthen their Generative AI knowledge, and improve career readiness for high-demand AI and cloud roles.
Transforming your team
Gain hands-on experience building production-ready RAG applications on AWS.
Develop expertise in Amazon Bedrock, vector search, and serverless AI deployment.
Learn enterprise security, monitoring, and cost optimization for Generative AI workloads.
Build a capstone project and earn an industry-recognized certification to enhance career opportunities.
CERTIFICATION
Earn a certificate on completion of this course
After finishing Nevolearn's Build RAG on AWS Cloud course, you'll earn an industry-recognized professional certificate. This certificate is designed for sharing on LinkedIn, allowing you to highlight your accomplishments and share your new skills with your network.
Validating your expertise with a professional certification helps you stand out in the job market and provides tangible proof of your commitment to continuous learning and professional growth.
TESTIMONIALS
What Learners are Saying
Still have a question? Get in Touch with our Experts
The demand for cloud-native AI applications is increasing as organizations adopt LLM-powered systems for enterprise knowledge retrieval, customer support automation, and intelligent search applications. This specialized AWS RAG Training equips professionals with practical skills to Build RAG on AWS using AWS managed services.
The course covers end-to-end RAG Architecture on AWS, including document storage in Amazon S3, embedding generation with Amazon Bedrock, vector indexing using OpenSearch, serverless orchestration with AWS Lambda, API integration through API Gateway, and monitoring with CloudWatch.
Through hands-on labs and real-world implementation, learners gain expertise in Amazon Bedrock Training workflows, OpenSearch Vector Database Training, and secure AWS Lambda AI Integration for scalable AI deployments.
This AWS Generative AI Course prepares professionals for enterprise AI roles and strengthens practical knowledge for AWS AI Certification and advanced cloud AI architecture roles. By the end of the program, learners will confidently deploy enterprise-grade Retrieval-Augmented Generation applications on AWS Cloud.
Retrieval-Augmented Generation Course.
Modern enterprises are rapidly adopting Generative AI applications that require accurate, scalable, and secure knowledge retrieval systems. This AWS RAG Training helps professionals understand how to Build RAG on AWS using production-ready managed services like Amazon Bedrock, OpenSearch, Lambda, API Gateway, and S3.
This AWS Generative AI Course is designed for developers, cloud engineers, and AI professionals who want hands-on experience with RAG Architecture on AWS. Participants learn document ingestion, embedding generation, vector indexing, retrieval orchestration, and LLM response generation using AWS-native services.
The program includes practical implementation of Amazon Bedrock Training, OpenSearch Vector Database Training, and AWS Lambda AI Integration for building scalable AI applications. Learners also gain exposure to enterprise security, IAM policies, monitoring, and cost optimization strategies.
Completing this Retrieval-Augmented Generation Course strengthens cloud AI deployment skills and improves career opportunities through industry-recognized AWS AI Certification pathways.