AWS Generative AI with Amazon Bedrock Course in Hyderabad
Build secure generative AI applications and agents on Amazon Bedrock
The AWS Generative AI with Amazon Bedrock course shows you how to design, build and operate generative AI applications on AWS. You start with how large language models work, prompt design and the Bedrock model catalogue, then build retrieval-augmented generation applications with Bedrock Knowledge Bases and vector stores, add guardrails for safety, and create agents that call tools and APIs. You also learn evaluation, observability, cost and latency optimisation, and security with IAM, VPC endpoints and encryption. Hands-on labs use Python and the AWS SDK, with AWS Lambda, API Gateway, Amazon S3 and Amazon OpenSearch Service or Aurora PostgreSQL with pgvector. The course is aimed at developers, data professionals and cloud engineers who know basic Python and have used AWS. It does not claim to be a certification course, although the topics are relevant to AWS generative AI certifications such as AWS Certified AI Practitioner and the AWS Certified Generative AI Developer – Professional exam, whose current details you should confirm with AWS. Classes run in Ameerpet, Hyderabad, and online.
Syllabus
Generative AI & LLM Fundamentals
- •How large language models work: tokens, context window and sampling parameters
- •Foundation models, fine-tuning, RAG and agents: when to use each
- •Limitations: hallucination, bias and data privacy
- •Lab: Explore model behaviour with different temperature and top-p settings
- •Outcome: Explain generative AI concepts and limits to stakeholders
AWS Foundations for AI Builders
- •IAM roles and permissions for Bedrock; enabling model access in a Region
- •AWS SDK for Python (boto3), AWS CLI and Lambda basics
- •Pricing: on-demand and provisioned throughput, token cost estimation
- •Lab: Set up a secure development environment and call Bedrock from Python
- •Outcome: A working, least-privilege Bedrock environment
Amazon Bedrock Models & APIs
- •Bedrock model providers and how to choose a model
- •InvokeModel and Converse APIs, streaming responses and token limits
- •Multimodal inputs and image generation overview
- •Lab: Build a chat client with streaming and conversation history
- •Outcome: Select and call models through the Bedrock API
Prompt Engineering & Prompt Management
- •Prompt patterns: role, few-shot, chain of reasoning and structured JSON output
- •Prompt templates, variables and Bedrock Prompt Management
- •Prompt injection awareness and defensive prompting
- •Lab: Build a document-summary service with structured output
- •Outcome: Write reliable, versioned prompts for production use
Embeddings & Vector Stores
- •Text embeddings and similarity search
- •Chunking strategies and metadata filtering
- •Vector stores on AWS: Amazon OpenSearch Service, Aurora PostgreSQL with pgvector and other options
- •Lab: Embed a document set and run semantic search
- •Outcome: Build a vector index that returns relevant passages
Retrieval-Augmented Generation with Knowledge Bases
- •Bedrock Knowledge Bases: data sources, parsing and ingestion
- •Retrieve and RetrieveAndGenerate APIs with source citations
- •Hybrid search and re-ranking concepts; tuning chunk size and top-k
- •Lab: Build a question-answering assistant over company PDFs
- •Outcome: Deliver grounded answers with citations
Guardrails & Responsible AI
- •Amazon Bedrock Guardrails: content filters, denied topics, word filters and sensitive information redaction
- •Contextual grounding checks and hallucination reduction
- •Fairness, transparency and human-in-the-loop design
- •Lab: Add guardrails to the RAG assistant and test with adversarial prompts
- •Outcome: Make an AI application safer to release
Agents, Tools & Workflows
- •Agent concepts: planning, tool calling and memory
- •Amazon Bedrock Agents, action groups with Lambda and OpenAPI schemas
- •Orchestration with AWS Step Functions and event triggers
- •Lab: Build an agent that looks up orders and drafts a reply
- •Outcome: Create agents that safely act on business systems
Customisation: Fine-Tuning & Model Evaluation
- •Prompting versus RAG versus fine-tuning versus continued pre-training: decision criteria
- •Preparing training data and Bedrock customisation jobs (overview)
- •Model evaluation: automatic metrics, LLM-as-judge and human review
- •Lab: Create an evaluation dataset and compare two models
- •Outcome: Choose and justify a model and approach with evidence
Application Architecture & Deployment
- •Reference architectures for chatbots and document assistants
- •Amazon API Gateway, AWS Lambda, Amazon DynamoDB for sessions and Amazon S3 for documents
- •Front-end integration with a simple web UI
- •Lab: Deploy a serverless generative AI API
- •Outcome: Ship a generative AI feature as a scalable service
Observability, Cost & Performance
- •Model invocation logging, CloudWatch metrics and tracing
- •Token usage dashboards and budget alarms
- •Latency and cost optimisation: smaller models, prompt caching, batching and routing
- •Lab: Build a cost and latency dashboard for the application
- •Outcome: Operate generative AI workloads within budget and latency targets
Security, Governance & Capstone
- •VPC endpoints and PrivateLink for Bedrock, KMS encryption and CloudTrail audit
- •Data privacy, retention and compliance considerations
- •Capstone: end-to-end RAG assistant with guardrails, agent action and monitoring
- •Capstone presentation and portfolio write-up
- •Outcome: A deployed, documented generative AI application
Tools Covered
Projects Included
- Document Q&A Assistant: Build a RAG assistant over PDF policies with Bedrock Knowledge Bases and citations; deliverable is a web demo and evaluation sheet.
- Guardrailed Customer-Support Bot: Add guardrails, PII redaction and grounding checks to a chat assistant; deliverable is a test report on adversarial prompts.
- Order-Status Agent: Build a Bedrock agent with a Lambda action group that queries a DynamoDB table; deliverable is a working agent and API schema.
- Prompt and Model Evaluation Harness: Compare two models on a test dataset using automatic metrics and human review; deliverable is a comparison report with recommendations.
- Capstone Serverless GenAI API: Deploy an API with API Gateway, Lambda, Bedrock and monitoring; deliverable is a GitHub repository, architecture diagram and cost estimate.
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Call: +91 9908099980Prerequisites & Eligibility
Understand the foundation and skills needed to succeed in the AWS Generative AI with Amazon Bedrock course.
Course Prerequisites
- Any graduate is eligible for enrollment across all program tracks
- Dedication to work on real-time scenario projects and live client project implementations
- Willingness to engage in aptitude, soft skills and interview readiness sessions
- Commitment to complete full-day training or intensive/internship program schedules
Why Choose GloryTecks
Build practical capabilities through structured learning, expert mentors and hands-on projects.
5+ Years of Excellence
Established training institute in Hyderabad with a proven track record of producing industry-ready professionals.
Expert Faculty
Learn from trainers with real-world industry experience, who teach the way the work is actually done.
Placement Assistance
A dedicated placement team with tie-ups across hiring companies. We support you until you get hired.
Comprehensive Curriculum
Updated syllabus covering the latest technologies and industry best practices, with hands-on projects.
Career Growth Focus
Not just training, but complete career transformation with soft skills and interview preparation.
Online & Classroom
Attend live online batches or classroom training at our Ameerpet centre, whichever suits you.
Flexible Batches
Weekday, weekend and online batch timings to suit working professionals, students and freshers.
Real-time Projects
Work on real-time scenario projects so you finish with practical experience to show employers.
We provide placement support with direct access to hiring companies, along with comprehensive training that helps freshers become job-ready professionals.
Certification
Explore certification and practical learning opportunities with a focus on AWS Generative AI with Amazon Bedrock.
Certificate of Completion
AWS Generative AI with Amazon Bedrock
This certifies that
[Your Name]
has successfully completed the
AWS Generative AI with Amazon Bedrock
Training Program
GloryTecks
Ameerpet, Hyderabad
Certificate ID
GT-YYYY-XXXX
Certificate Includes
- Course completion certificate from GloryTecks
- Project completion certificates
- Internship completion certificate for eligible internship programs
- Digital certificate with a unique verification ID
- Shareable on LinkedIn and other professional platforms
- Valid proof of skill acquisition for employers
- Includes the detailed syllabus covered
Industry Focus
A certificate built around the tools and skills employers ask for, adding credibility to your resume.
Skill Validation
Proves your expertise in the course and the technologies you worked with.
Career Advancement
Strengthens your profile when you apply for jobs and negotiate your salary.
Digital & Physical
Get a digital certificate for online sharing and a physical copy for framing.
Course Certificate
Upon successful completion of the training program
Project Certificate
For major projects completed during training
Course Features & Highlights
Build a strong foundation through a structured AWS Generative AI with Amazon Bedrock course with practical learning and hands-on exposure.
Live Projects
Work on real-world projects from day one with industry use cases.
Expert Trainers
Learn from industry professionals with real-world experience.
Placement Support
Dedicated placement team with assistance until you get hired.
Industry Certification
Get certified and boost your resume with recognized credentials.
LMS Access
Access to learning materials and recorded sessions.
Mock Interviews
Weekly mock interviews to prepare you for real job scenarios.
Resume Building
Professional resume preparation and LinkedIn profile optimization.
Additional Benefits
- Flexible Payment Plans
- 6-12 Months LMS Access
- Doubt Clearing Sessions
- 24/7 Learning Support
Frequently asked questions
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Best AWS Generative AI with Amazon Bedrock Training in
Ameerpet, Hyderabad
Located at Ameerpet — Hyderabad’s IT training hub. Easily accessible from all areas of Hyderabad. Just 2 minutes walk from Ameerpet Metro Station.
Placement Support
Career support covers resume and LinkedIn preparation, mock interviews and a portfolio of 5 course projects. Roles that use these skills include Generative AI Developer, AI Engineer, Cloud Engineer (AI), Solutions Engineer; support is guidance with the job search, not a guarantee of a job.