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Enterprise Generative AI Systems on AWS Certification Course

Name: Enterprise Generative AI Systems on AWS Certification Course
Availability: InStock
Rating: 1 (11 reviews)
Author: School of AI

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Design, Secure, Scale, and Govern Production-Ready Generative AI, RAG, Agents, and Multimodal Systems on AWS

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(11) Ratings

0 students

IT & Software

Created by School of AI

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What you'll learn

Design complete, production-ready enterprise Generative AI architectures on AWS from user channels through models, data, security, and operations.
Build Generative AI applications using Amazon Bedrock, Amazon Nova, Anthropic Claude, Meta Llama, Mistral, and other foundation models.
Create secure Retrieval-Augmented Generation systems using Bedrock Knowledge Bases, Amazon OpenSearch Serverless, S3 Vectors, Aurora PostgreSQL and GraphRAG.
Design and build enterprise AI agents using Bedrock Agents, AgentCore, Strands Agents SDK, LangChain, LangGraph, Step Functions, and Bedrock Flows.
Connect Generative AI systems to enterprise data stored in Amazon S3, Aurora, RDS, DynamoDB, Redshift, SaaS applications, internal APIs, and on-premises systems
Build scalable application and API layers using Route 53, CloudFront, AWS WAF, API Gateway, Lambda, ECS, Fargate, App Runner, and EKS.
Create batch, streaming, and event-driven data-ingestion pipelines using AWS Glue, AppFlow, DataSync, EventBridge, SQS, Kinesis, Lambda, and Step Functions.
Process, chunk, enrich, embed, and index documents, images, audio, video, tables, forms, and other multimodal enterprise content.
Protect AI applications against prompt injection, unsafe outputs, data exposure, unauthorized tool execution, hallucinations, and malicious retrieved content.
Implement enterprise identity, authorization, governance, monitoring, evaluation, DevOps, cost management, audit logging, backup, and disaster recovery controls

This course includes:

62.5 total hours on-demand video

0 articles

0 downloadable resources

492 lessons

Full lifetime access

Access on mobile and TV

Certificate of completion

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Course content

Requirements

No previous Generative AI or machine-learning experience is required.
A basic understanding of cloud computing concepts will be helpful but is not mandatory.
Familiarity with AWS services is useful, although all major architecture components are explained during the course.
Basic programming or API knowledge may help with hands-on activities, but the architecture lessons are suitable for non-developers.
An AWS account is recommended for students who want to complete the practical labs.
Access to a modern computer, internet connection, and web browser is required.
Students should be comfortable learning through architecture diagrams, service comparisons, practical scenarios, and hands-on exercises.
A willingness to explore security, governance, data, application, and operational considerations across the complete AI lifecycle is the most important prerequisite.

Description

Post Views: 2

This course contains the use of artificial intelligence.

Build the skills to design, secure, deploy, and operate enterprise generative AI systems on AWS through a complete architecture-first learning experience. This course takes you from business requirements and interaction channels to production-ready platforms powered by Amazon Bedrock, foundation models, retrieval-augmented generation, AI agents, enterprise data, security controls, observability, and automation.

You will begin by exploring how large organizations such as Netflix, United Airlines, Walmart, and Tesla could apply AWS Generative AI architecture to real business scenarios. You will then learn how to read a complete architecture from left to right, understand the prompt-and-response lifecycle, identify trust boundaries, map data movement, and apply the AWS Well-Architected Generative AI Lens.

The course covers web, mobile, Slack, Microsoft Teams, APIs, and Amazon Connect experiences. You will learn how to connect structured and unstructured enterprise data from Amazon S3, Aurora, RDS, DynamoDB, Redshift, SaaS platforms, internal APIs, SharePoint, Salesforce, ServiceNow, and on-premises systems.

You will design secure application layers using Route 53, CloudFront, AWS WAF, Shield, API Gateway, Lambda, ECS, Fargate, App Runner, and EKS. You will compare synchronous and asynchronous workflows, serverless and container-based architectures, stateless and stateful services, and resilient patterns for scaling, retries, timeouts, and long-running AI tasks.

A major focus is Amazon Bedrock, including model selection, Amazon Nova, Anthropic Claude, Meta Llama, Mistral, embeddings, multimodal models, structured outputs, tool calling, prompt routing, model customization, and cost-aware inference. You will build RAG systems with Bedrock Knowledge Bases, OpenSearch Serverless, S3 Vectors, Aurora PostgreSQL with pgvector, and Neptune Analytics for GraphRAG.

You will also design agentic AI systems using Bedrock Agents, AgentCore, Strands Agents SDK, LangChain, LangGraph, Step Functions, and Bedrock Flows. Topics include planning, tool execution, memory, human approval, permission boundaries, error recovery, loop prevention, and enterprise automation.

The data engineering modules show you how to create ingestion pipelines with AWS Glue, AppFlow, Database Migration Service, DataSync, EventBridge, SQS, Kinesis, Lambda, and Step Functions. You will process documents with Amazon Textract and Bedrock Data Automation, preserve metadata, select chunking strategies, generate embeddings, synchronize knowledge bases, and measure retrieval quality. You will also learn how to evaluate model responses, groundedness, safety, agent decisions, and task completion using automated metrics, LLM-as-a-judge methods, human reviews, regression datasets, and release quality gates.

Security and governance are integrated throughout the course. You will implement IAM, Cognito, least-privilege access, Bedrock Guardrails, prompt-injection defenses, sensitive-data protection, VPC isolation, encryption, audit logging, responsible AI reviews, and compliance evidence.

Finally, you will master monitoring, evaluation, CI/CD, infrastructure as code, caching, cost management, backup, disaster recovery, and multi-region resilience. Hands-on labs and a comprehensive capstone guide you through designing a secure, scalable, reliable, observable, and governed AWS GenAI platform ready for enterprise use.

By the end, you will translate business requirements into defensible architecture decisions and confidently communicate AWS GenAI designs clearly to engineering, security, risk, operations, and leadership teams.

This course is ideal for cloud architects, AI engineers, developers, security professionals, technical leaders, and anyone preparing to build production-grade Generative AI, RAG, and AI agent solutions on AWS.

Courze

Who this course is for:

Cloud architects who want to design secure and scalable Generative AI platforms on AWS.
AI engineers and machine-learning engineers building production applications with Amazon Bedrock.
Software developers who want to create RAG applications, AI agents, copilots, and multimodal AI systems.
Solutions architects preparing to recommend AWS services for enterprise AI use cases.
Data engineers responsible for connecting, processing, enriching, and indexing enterprise information for AI applications.
Security, governance, risk, and compliance professionals responsible for protecting and governing Generative AI systems.
DevOps, platform, and site reliability engineers supporting the deployment and operation of production AI workloads.
Technical leaders, consultants, and enterprise architects responsible for AI strategy and architecture decisions.
AWS professionals who want to expand their skills into Generative AI, RAG, agents, and foundation-model platforms.
Anyone who wants to understand how a complete enterprise AWS Generative AI system works from the user request to the final response or automated action.