Machine Learning and Artificial Intelligence have become strategic priorities for modern technology teams, but workplace demands are moving beyond chatbot demos toward sophisticated agentic AI systems that can operate on cloud infrastructure.
This specialization brings together the practical skills learners expect from an Agentic AI course with AWS cloud architecture. This way, it creates a focused pathway from AI application development to cloud-ready systems. Unlike a standalone AI agent course, it connects agent development with cloud-native backend architecture and deployment patterns.
Designed for software developers and AI engineers, the specialization starts off with Agentic AI fundamentals, where you’ll build AI applications with MERN, RAG, MCP, and leading AI models while learning how agents use context and tools. Next, you’ll progress to MCP servers, embeddings, tool calling, and agent orchestration to create more capable AI workflows.
Finally, you’lllearn AWS database management, serverless services, messaging, and event-driven architecture and apply these skills to design scalable cloud-based AI systems.
By the end, you’ll be able to build AI systems that retrieve relevant context, execute tools, coordinate backend actions, and connect to cloud-native services with ease.
Enroll now and move beyond AI prototypes to build cloud-ready Agentic AI systems!
Projet d'apprentissage appliqué
Throughout this specialization, you’ll move from understanding agentic AI foundations to building AI-enabled applications and connecting them to cloud-native backend patterns. You will also:
Build a MERN-based AI chat application with React, Node.js, TypeScript, OpenAI, and Gemini integrations, creating a responsive interface.
Build a practical RAG pipeline by working with embeddings, retrieval, and augmentation to give AI applications access to relevant contextual information.
Create an MCP server with tools, resources, prompts, transport, sessions, and backend services, then connect it to an MCP client for AI-driven execution.
Work with ChromaDB, pgVector, PostgreSQL, embeddings, and retrieval workflows to build more context-aware AI systems.
Design AWS-backed architectures using RDS, Aurora, DynamoDB, Lambda, Step Functions, SQS, SNS, Kinesis, & other managed services.
Apply AWS security and identity concepts using Cognito, IAM, STS, KMS, Parameter Store, and Secrets Manager.
