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Enterprise AI Platforms with AWS, Azure & Google Cloud

Master enterprise-grade Generative & Agentic AI deployment using AWS Bedrock, Google Vertex AI, and Azure AI Foundry.

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Prerequisites for Enterprise AI Platforms

Prerequisites and Eligibility

No prior deep cloud expertise is mandatory.

However, a basic understanding of:

  • APIs
  • software applications
  • cloud concepts
  • or AI workflows

can be helpful.

The course is designed to progressively introduce enterprise AI platform concepts through hands-on labs and guided implementations.

Prerequisites and Eligibility
  • 500K+
    Professionals trained
  • 250+
    Workshops every month
  • 300+
    Agile transformations

Enterprise AI Platforms Highlights

Enterprise AI Platforms Course Highlights

16 Hours Live Instructor-led Training

Real-world Enterprise Use Cases

Hands-on Labs & Deployments

AI Guardrails & Governance

Multi-cloud AI Engineering

Enterprise RAG Systems

Multi-Agent Architectures

The Enterprise AI Platforms with AWS, Azure and Google Cloud program is designed for professionals looking to master enterprise-grade Generative AI and Agentic AI deployment using the world’s leading cloud AI ecosystems. The course provides hands-on exposure to AWS Bedrock, Google Vertex AI, and Azure AI Foundry, enabling learners to build production-ready AI agents, enterprise RAG systems, multi-agent architectures, and scalable AI workflows. As organizations rapidly move from AI experimentation to full-scale production deployment, this program helps learners understand how enterprises architect, govern, secure, and operationalize modern AI systems.

Through live instructor-led sessions, hands-on labs, and real-world implementation projects, learners gain practical experience with enterprise AI engineering workflows, AI infrastructure, observability, AI governance, and cloud-native AI deployment patterns. The curriculum covers advanced topics such as Bedrock Guardrails, Vertex AI Agent Builder, Azure AI Search, multi-agent orchestration, AI security, prompt pipelines, and LLMOps practices. Participants will build enterprise AI solutions including multi-model AI chat systems, AI-powered data analysis agents, RAG pipelines, and multi-agent automation workflows using production-grade cloud AI services.

The course is ideal for AI engineers, cloud professionals, enterprise architects, DevOps engineers, digital transformation leaders, and technical teams seeking to specialize in enterprise AI deployment and multi-cloud AI engineering. By the end of the program, learners will be equipped to design scalable AI architectures, deploy secure enterprise AI systems, implement AI governance frameworks, evaluate cloud AI platforms, and build compliant AI solutions for modern enterprise environments. The program also prepares professionals for emerging roles such as AI Platform Engineer, Enterprise AI Engineer, LLMOps Engineer, GenAI Solutions Architect, and Multi-Cloud AI Engineer.

Why Enterprise AI & Multi-Cloud AI Skills Matter in 2026

Rising demand for Enterprise AI Platform Professionals
Salary
Min
Average
Max
Hiring Companies
Accenture
Allianz
BOA
Bosch
Danske
Comcast
Investment
$ 2.5 Trillion
Worldwide AI spending to reach $2.5 trillion by 2026

Organizations across industries are rapidly moving from isolated AI experimentation to large-scale enterprise AI deployment. The next phase of AI adoption is no longer limited to chatbots and prompt engineering. Enterprises are now focused on deploying production-grade AI systems, orchestrating intelligent agents, implementing enterprise RAG architectures, governing AI responsibly, and scaling AI securely across cloud ecosystems. As a result, there is a growing demand for professionals who understand enterprise AI platforms such as AWS Bedrock, Google Vertex AI, and Azure AI Foundry, along with critical capabilities like multi-agent systems, LLMOps, observability, AI governance, and cloud-native AI deployment patterns.

Enterprise investment in Generative AI is accelerating at an unprecedented pace. Industry reports indicate that enterprise Generative AI spending grew from approximately $11.5 billion in 2024 to nearly $37 billion in 2025, reflecting massive year-over-year growth. Gartner forecasts worldwide AI spending to exceed $2.5 trillion by 2026, while IDC predicts enterprise AI investments could cross $632 billion by 2028. These investments are being driven heavily by enterprise AI infrastructure, cloud AI platforms, AI automation systems, and scalable deployment architectures. Enterprises are no longer experimenting with AI in isolated pilots — they are operationalizing AI across customer systems, enterprise workflows, cloud infrastructure, and intelligent business operations.

At the same time, cloud AI platforms are becoming the foundation of modern enterprise AI engineering. Organizations increasingly rely on managed AI ecosystems because they require scalability, governance, security, observability, and production-ready deployment capabilities that traditional AI environments often lack. The Cloud AI market is projected to grow from nearly $89 billion in 2025 to more than $269 billion by 2031, while the AI platform market itself is expected to expand beyond $94 billion by 2030. As enterprises standardize AI deployment using AWS Bedrock, Vertex AI, Azure AI Foundry, and related cloud AI ecosystems, demand is rising sharply for professionals who can securely deploy AI systems, build enterprise RAG pipelines, architect cloud-native AI applications, govern AI responsibly, and scale AI solutions across business units and global teams.

Why KnowledgeHut for Enterprise AI Platforms

The KnowledgeHut Advantage

Comprehensive Curriculum

Structured modules covering fundamental and advanced concepts of key concepts.

Immersive Learning

Learn, practice, gain insights and apply skills to drive change and unlock new possibilities.

Interactive Experience

Engage with peers and instructors through forums and discussion boards.

Practical Use Cases

Analyze and learn from real-world case studies and success stories.

Networking Opportunities

Connect with fellow course graduates and professionals in the field.

Learn from Experienced Trainers

Learn from industry leaders and get practical understanding based on real experience.

Who can attend the Enterprise AI Platforms Course

Who This Course Is For
  • AI Engineers
  • Cloud Engineers
  • ML Engineers
  • DevOps Engineers 
  • Platform Engineers
  • Software Developers
  • CTO Office Teams
  • Enterprise Architects
  • Innovation Leaders
  • AI Transformation Consultants
  • Digital Transformation Leaders
  • AWS Professionals
  • Azure Engineers
  • GCP Professionals 
Whoshouldlearn image

What You Will Learn in Enterprise AI Platforms Course

Learning Objectives
1
Enterprise AI Deployment

Deploy secure, scalable, and production-ready enterprise AI systems across modern cloud platforms and business environments.

2
Production AI Agents

Build intelligent production-grade AI agents capable of reasoning, automation, tool usage, and enterprise workflow orchestration.

3
Secure RAG Architectures

Design secure Retrieval-Augmented Generation (RAG) systems with enterprise search, vector databases, and knowledge pipelines.

4
AI Governance & Guardrails

Implement AI guardrails, responsible AI policies, content filtering, PII protection, and enterprise governance frameworks.

5
Cloud AI Platform Evaluation

Evaluate and compare leading cloud AI platforms including AWS Bedrock, Vertex AI, and Azure AI Foundry for enterprise use cases.

6
Multi-Agent Workflow Engineering

Build advanced multi-agent workflows involving supervisor agents, specialist agents, orchestration logic, and collaborative AI systems.

Enterprise AI Platforms Projects

Build Real Enterprise AI Systems
These projects are intentionally designed to reflect real-world Enterprise AI Platforms used in modern AI systems and enterprise environments.
Multi-Model AI Chat System
Multi-Model AI Chat System

Multi-Model AI Chat System

Build a unified AI interface across Claude, Nova, and Llama models using Bedrock Converse API.
Enterprise RAG Pipeline
Enterprise RAG Pipeline

Enterprise RAG Pipeline

Create a production-ready RAG system with document ingestion, vector search, and hybrid retrieval.
Multi-Agent AI Workflow
Multi-Agent AI Workflow

Multi-Agent AI Workflow

Build supervisor agents coordinating multiple specialist agents for enterprise automation.
AI Data Analysis Agent
AI Data Analysis Agent

AI Data Analysis Agent

Deploy a code-interpreter AI agent capable of analyzing live datasets using sandboxed Python execution.
AI Governance & Guardrails
AI Governance & Guardrails

AI Governance & Guardrails

Configure enterprise AI security policies, PII protection, and responsible AI filters.
Azure AI Enterprise Agent
Azure AI Enterprise Agent

Azure AI Enterprise Agent

Deploy enterprise-grade AI agents using Azure AI Foundry and Azure AI Search.

Enterprise AI Platforms Curriculum

Curriculum

1. Enterprise AI Platform Foundations

Introduction to Enterprise AI Infrastructure

  • Understanding the Enterprise AI ecosystem
  • Evolution of cloud-native AI platforms
  • Enterprise AI deployment patterns
  • Managed AI infrastructure vs self-hosted AI systems

AWS Bedrock Fundamentals

  • AWS Bedrock overview
  • Managed LLM APIs
  • Models, pricing, quotas, and architecture
  • Bedrock model access:
    • Nova
    • Claude 3.x
    • Llama 3
    • Mistral
    • Cohere

Bedrock Converse API

  • Unified multi-model interfaces
  • Tool-use capabilities
  • Multi-model orchestration
  • Enterprise conversational AI architectures

Google Vertex AI Overview

  • Understanding the Google Cloud AI stack
  • Gemini models and multimodal capabilities
  • Model Garden overview
  • Long-context AI applications

Azure AI Foundry Overview

  • Unified AI development environment
  • Azure OpenAI Service
  • GPT-4o, DALL-E, and Whisper deployment
  • Enterprise AI deployment workflows

2. Enterprise RAG & Knowledge Systems

Bedrock Knowledge Bases

  • Managed RAG architectures
  • S3 document ingestion
  • OpenSearch integration
  • Aurora pgvector integration

Advanced RAG Features

  • Metadata filtering
  • Hybrid search
  • Custom chunking strategies
  • Retrieval optimization

Vertex AI RAG Engine

  • Managed retrieval systems
  • Vertex Vector Search
  • Grounded AI applications
  • Enterprise datastore integration

Azure AI Search

  • Hybrid RAG implementations
  • Cognitive skills
  • Semantic ranking
  • Enterprise document search systems

Knowledge Architecture Design

  • Enterprise document pipelines
  • Vector database selection
  • Multi-cloud retrieval strategies
  • Scalable knowledge management systems

3. Agentic AI Systems & Multi-Agent Architectures

AgentCore Architecture

  • Managed agent runtime on AWS
  • Enterprise agent orchestration
  • Agent lifecycle management

Agent Memory Systems

  • Short-term memory
  • Long-term memory stores
  • Context persistence strategies

Agent Tools & Action Groups

  • Built-in tools
  • Custom Lambda-backed tools
  • OpenAPI schema integration
  • Tool orchestration patterns

Multi-Agent Systems

  • Supervisor agents
  • Specialist sub-agents
  • Multi-agent collaboration models
  • Delegation workflows

Inline & Runtime Agents

  • Ephemeral agents
  • Dynamic agent configuration
  • Runtime orchestration

AgentCore Code Interpreter

  • Sandboxed Python execution
  • Data analysis agents
  • AI-assisted analytical workflows

Browser-based AI Agents

  • Web navigation agents
  • Data extraction workflows
  • Autonomous browser interactions

4. Enterprise AI Governance, Security & Observability

AI Security & Governance

  • IAM policies
  • KMS encryption
  • VPC endpoints
  • Secure enterprise AI deployment

Bedrock Guardrails

  • Content filtering
  • PII redaction
  • Grounding checks
  • Denied topic enforcement

AI Observability & Monitoring

  • CloudWatch integration
  • X-Ray tracing
  • Agent reasoning inspection
  • Enterprise monitoring workflows

Prompt Flows & Deterministic Pipelines

  • Bedrock Flows
  • Prompt Flow on Azure
  • Visual AI workflow builders
  • Deterministic AI pipelines

Model Evaluation & Fine-Tuning

  • Batch inference jobs
  • Model evaluation workflows
  • Continued pre-training
  • Instruction fine-tuning

Cost Optimization Strategies

  • Provisioned throughput
  • On-demand vs batch inference
  • Cloud AI cost governance
  • Enterprise AI optimization models

5. Multi-Cloud AI Engineering & Enterprise Deployment

Vertex AI Agents

  • Tool-use agents
  • Grounded AI systems
  • Agent evaluation workflows

Vertex AI Pipelines & MLOps

  • AI application pipelines
  • MLOps fundamentals
  • Enterprise deployment automation

Azure AI Agents Service

  • File-search agents
  • Code-interpreter agents
  • Enterprise AI workflows

Azure Content Safety

  • Responsible AI filters
  • AI risk monitoring
  • Governance workflows

Multi-Cloud AI Architectures

  • Cloud portability patterns
  • Platform comparison frameworks
  • Enterprise AI decision-making
  • Vendor-neutral deployment strategies

Enterprise AI Deployment Strategy

  • Choosing the right AI platform
  • Compliance and governance considerations
  • Scalability planning
  • Production AI architecture patterns

Hands-On Labs Included

Throughout the course, learners will work on real-world enterprise AI deployment labs, including:

  • Building a unified multi-model AI chat interface
  • Creating enterprise RAG pipelines
  • Deploying AI agents with custom action groups
  • Building multi-agent orchestration workflows
  • Configuring enterprise AI guardrails
  • Creating AI-powered data analysis agents
  • Deploying Vertex AI grounded agents
  • Building Azure AI Foundry enterprise workflows

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Enterprise AI Platforms Course FAQs

Frequently Asked Questions
Course FAQs

1. What is an Enterprise AI Platform?

An Enterprise AI Platform is a cloud-based ecosystem that enables organizations to build, deploy, manage, secure, and scale AI applications in production environments. These platforms provide services for Generative AI, AI agents, model deployment, RAG systems, AI governance, observability, and enterprise integrations.

Leading enterprise AI platforms today include:

  • AWS Bedrock
  • Google Vertex AI
  • Azure AI Foundry

These platforms help enterprises accelerate AI adoption while ensuring scalability, security, and compliance.

2. What is AWS Bedrock?

AWS Bedrock is Amazon Web Services’ managed Generative AI platform that allows organizations to access foundation models from providers such as Anthropic Claude, Meta Llama, Mistral, Cohere, and Amazon Nova through a unified API.

AWS Bedrock enables enterprises to:

  • Build AI applications
  • Create AI agents
  • Deploy RAG systems
  • Configure AI guardrails
  • Fine-tune models
  • Manage enterprise AI workloads securely

without managing underlying infrastructure.

3. What is Vertex AI?

Vertex AI is Google Cloud’s unified AI platform designed for building and deploying machine learning and Generative AI applications.

Vertex AI provides:

  • Gemini models
  • AI Agent Builder
  • RAG engines
  • Vector search
  • MLOps pipelines
  • AI evaluation tools

It is widely used for enterprise AI deployments requiring multimodal capabilities, search grounding, and scalable AI infrastructure.

4. What is Azure AI Foundry?

Azure AI Foundry is Microsoft’s enterprise AI development platform that enables organizations to build, test, deploy, and govern AI applications at scale.

It includes:

  • Azure OpenAI Service
  • GPT-4o deployment
  • AI agents
  • Prompt Flow
  • AI Search
  • Responsible AI monitoring
  • Enterprise AI governance tools

Azure AI Foundry is designed for organizations already operating within the Microsoft ecosystem.

5. What are AI agents?

AI agents are intelligent software systems capable of performing tasks autonomously using reasoning, memory, tools, APIs, and workflows.

Unlike traditional chatbots, AI agents can:

  • Make decisions
  • Execute multi-step tasks
  • Use external tools
  • Retrieve information
  • Collaborate with other agents
  • Interact with applications and APIs

AI agents are becoming a foundational component of enterprise AI systems.

6. What is Agentic AI?

Agentic AI refers to AI systems capable of autonomous reasoning, planning, decision-making, and task execution.

Agentic AI systems can:

  • Break complex tasks into steps
  • Use tools dynamically
  • Coordinate workflows
  • Maintain memory and context
  • Collaborate across multiple agents

This approach is driving the next generation of enterprise automation and intelligent workflows.

7. What is a RAG architecture?

RAG (Retrieval-Augmented Generation) is an AI architecture that combines:

  • Large Language Models (LLMs) 
    with
  • External enterprise knowledge sources

Instead of relying only on pre-trained model knowledge, RAG systems retrieve relevant organizational data in real time before generating responses.

RAG architectures are widely used for:

  • Enterprise chatbots
  • Knowledge assistants
  • AI search systems
  • Customer support AI
  • Internal AI copilots