AI BootCamps (7 Courses)
Course Category
Course Category
Google Rating
Professionals Upskilled
Live Cohorts Delivered
Enterprise Teams Trained
Overview
This 36-hour live Multi-cloud AI agents bootcamp teaches you to build, deploy and operate AI systems on Microsoft Azure, Amazon Web Services and Google Cloud. On each cloud, you deploy a machine learning model and a foundation model, build a retrieval-augmented generation (RAG) application, build a multi-agent system, and run it in production with tracing, guardrails and cost controls.
The shared ideas (RAG, agent design, MCP, A2A, guardrails and evaluation) are taught once in Module 1, so each cloud block goes straight to building. The same reference project is rebuilt on every cloud, which makes the differences between the platforms easy to see.
The bootcamp ends with a capstone: one multi-agent system whose agents run on different clouds and work together through the Agent2Agent (A2A) protocol.
Highlights
36 Hours. 4 Weeks. 23 Hands-On Labs. - 11 structured modules delivered through live cloud environments for practical, instructor-led learning.
Master Three Leading AI Agent Platforms - Build with Microsoft Foundry, Amazon Bedrock AgentCore and Gemini Enterprise Agent Platform.
Build the Same Project Across Three Clouds - Rebuild one reference project on Azure, AWS and Google Cloud to directly compare platforms and architectures.
Build. Deploy. Operate. - Follow the complete AI journey on every cloud, from model deployment and LLM applications to production-ready agents.
Connect Agents Across Clouds - Build a multi-cloud capstone where agents running on different clouds collaborate through the A2A protocol.
Build the Foundation for Three Certifications - Develop hands-on skills aligned with Microsoft AI-103, AWS AIP-C01 and Google Cloud Professional Agentic Architect.
Agentic AI Job Statistics
U.S. job postings mentioning Agentic AI skills
YoY growth in Agentic AI skill mentions
Median salary in Agentic AI job postings
Projected AI job postings in India in 2026
YoY growth in GenAI & LLM demand in India
YoY growth in AI Engineering demand in India
Curriculum
The three platforms side by side
Shared building blocks
Lab 1: accounts and reference project
Outcome
Microsoft Foundry setup and
architecture
Lab 2a: deploy ML and
foundation models
Lab 2b: RAG with Azure AI
Search
Guardrails and evaluation
Outcome
Lab 3a: build a single agent
Lab 3b: tools with MCP
Lab 3c: build a multi-agent workflow
Outcome:
Lab 4a: deploy agents
Lab 4b: observability and evaluation
Governance and cost
Outcome
Amazon Bedrock setup and
architecture
Lab 5a: deploy ML and foundation models
Lab 5b: RAG with Bedrock Knowledge Bases
Guardrails and evaluation
Outcome:
Lab 6a: build an agent with Bedrock Agents
Lab 6b: build an agent with Strands Agents
Lab 6c: build a multi-agent system
Outcome:
Lab 7a: deploy agents
Lab 7b: observability andevaluation
Governance and cost
Outcome:
Agent Platform setup and architecture
Lab 8a: deploy ML and foundation models
Lab 8b: RAG with RAG Engine
Guardrails and evaluation
Outcome:
Lab 9a: build an agent with ADK
Lab 9b: tools with MCP
Lab 9c: build a multi-agent system
Outcome:
Lab 10a: Deploy agents
Lab 10b: observability and evaluation
Governance and cost
Outcome:
Choosing a cloud
Lab 11: capstone build
Operate and compare
Demo and review
Outcome:
Schedules
Projects
Capstone Projects
Build 2 Capstone Projects live during the bootcamp based on your project selection, with access to 6 additional Capstone Projects on GitHub.
Build a multi-agent system with agents running across Azure, AWS and Google Cloud, connected through A2A. Compare cost and latency across clouds, then demonstrate your working solution.
Certification
Career Assistance
Your resume, LinkedIn, and GitHub — optimized by industry professionals to stand out to recruiters and land interviews faster.
Practice real technical and behavioural interviews with honest feedback from people who actually hire for AI roles.
Work directly with industry veterans to position yourself for AI roles — covering job search strategy, communication, and career planning.
Get direct visibility with active hiring managers. Understand what they actually look for — and how to stand out in competitive AI hiring pipelines.
Benefits
Testimonial
Finally, a bootcamp that goes beyond building agents. Working across Azure, AWS and Google Cloud helped me understand how to design, deploy and operate multi-agent systems in real enterprise environments.
The hands-on labs made the biggest difference. Rebuilding the same AI solution across three clouds gave me a much clearer understanding of architecture, platform choices, cost and deployment.
The multi-cloud capstone brought everything together. Connecting agents across different cloud platforms through A2A gave me practical experience that I could directly apply to enterprise AI projects.
FAQ
It builds the hands-on foundation for all three. Each exam covers more than the bootcamp, so plan for the exam-prep add-on for the one you sit.
Yes. Each cloud is also available as a standalone 12-hour course.
Azure, because Microsoft Foundry keeps models, agents, tracing and deployment in one place. AWS and Google Cloud follow. Private cohorts can change the order.
No. The ML models are pre-trained and the foundation models are built by their vendors. You deploy or enable them, then build the applications and agents.
You build one multi-agent system whose agents run on different clouds and call each other through A2A, then
compare cost and latency and demo it.
Labs use small models and short-lived resources, and Module 1 sets a budget alert on each cloud. Delete resources after each cloud block to stop charges.
Yes. Corporate cohorts are available with flexible scheduling.