Azure Open AI & Azure AI Foundry Training Course

AI is the future innovation with Agentic AI tools and frameworks (n8n, Auto-Gen, ChatGPT, Azure Open AI & Azure AI Foundry ) for intelligent automation. We are delivering this training in Gurgaon where-in core Gen AI principles using patterns like multi-agents, reflection & others will be taught using hands-on training to become a Gen AI Engineer.

4.8 Ratings

150+ Learners

Why Join Generative AI with Azure Open AI & Azure AI Foundry Course

Learn at your own will

Train using realtime course materials using online portals & trainer experience to get a personalized teaching experience.

Industry experience

Trained through leading industry professionals to get realtime project exposure

Realtime Industries Project exercises

Gain professionals insights through leading industry experts across domains

Personalized Q&A support

24/7 Q&A support designed to address training needs

Organizational Hiring Insights

Gen AI adoption via Agentic workflows

60%

Global Employment Opportunities

Top 10

Hiring Gen AI Skills

39% in IT

Azure Open AI & Azure AI Foundry Course

Kickstart your carrer in AI with certification in Generative AI with Azure Open AI & Azure AI Foundry Engineer course, that will help in shaping the carrer to the current industry needs that need automation using intelligent workflows like n8n, Microsoft AutoGen, LangGraph, Vector DB & others in every domain & sphere of the industry that will allow organizations to boost decision making & also thrive business growth with improved customer satisfaction.

Key Features

Self-paced videos & training materials with archived session recordings
Success Aimers targets industry employers to gain visibility
Industry-paced training with realtime scenarios using Agentic AI frameworks/tools (n8n, Microsoft AutoGen, Langchain, LangGraph, AWS Bedrock, Azure Open AI & Azure AI Foundry) for intelligent automation.
Real-World industry scenarios with projects implementation support
Live Virtual classes heading by top industry experts alogn with project implementation
Q&A support sessions
Job Interview preparation & use cases

Request More Information

Corporate Training

Enterprise training

About Azure Open AI Engineer Course

Explain Azure Open AI Engineer?

AI Engineers automate business processes via AI development frameworks (Agentic AI) like Azure AI Foundry, Azure Open AI, n8n, Microsoft AutoGen, Langchain, LangGraph, AWS Bedrock, Azure Open AI & others for intelligent automation that will allow organizations to boost decsion making & also thrive business growth with improved customer satisfaction. AI engineers build optimized workflows, deployment & maintain the AI development lifecycle to deliver high-précised solutions to the clients using Agentic AI frameworks/workflow builders.

Role of Azure Open AI Engineer?

Azure Open AI Engineers manages the end-to-end AI development lifecycle using Agentic AI frameworks and intelligent workflow strategies.
Responsibilities include:

Agentic AI Tools Adoption & automation using patterns like RAG ,Tools ,Reflection & others
Continuously exploring new AI based frameworks/tools to build optimized workflows
Deploying AI model within cloud infrastructure securly & seamlessly.
Develop & Design AI workflows that meets business needs with automation scripts
Success Aimers helps aspiring AI professionals to build, deploy, manage these Agents effectively seamlessly & to scale as per business needs.

Who should opt for Azure Open AI Engineer course?

Gen AI course accelerates/boost carrier in AI & technology-driven organizations

AI & Application Engineers – Develop Agentic AI workflows to automate business processes.
Data Engineers – Implementing Data Pipelines using AI
AI Developers – Automated workflows using Agentic AI frameworks/tools
AI Leaders – Leading AI Governance & initiative within enterprise.
Cloud & Infrastructure Engineers – Deploying AI developed applications seamlessly & effectively.

Prerequisites of Azure Open AI Engineer Course?

Prerequisites required for the Generative AI Engineer Certification Course

Below is the prerequisite to become a successful AI Professional:
High School Diploma or a undergraduate degree
Python Programming language
IT Foundational Knowledge along wit Data Science & Software Development skills
Knowledge of Cloud Computing Platforms like AWS, AZURE & GCP will be an added advantage.

Kind of Job Placement/Offers after Azure Open AI Engineer Certification Course?

Job Carrer Path in Azure Open AI & N8N Automation
Azure Open AI Engineer – Develop & Deploy AI developed solutions/applications using agentic frameworks/tools
Azure Open AI Engineer – Build context-aware, Enterprise ready AI systems.
AI Automation Engineer – Design, Developed & build automated workflows to drive key business processes/decisions
Cloud AI Engineer / AI Cloud Architect – Deploy AI Solutions seamlessly & effectively across enterprises with AI Governance strategy
AI Model Deployment Engineer – Automate Model Deployment & AI Lifecycle management via MLOps pipeline & Agentic AI Frameworks

Training Options for Azure Open AI Certification Course

Training Options	Weekdays (Mon-Fri)	Weekends (Sat-Sun)	Fast Track
Duration of Course	2 months	3 months	15 days
Hours / day	1-2 hours	2-3 hours	5 hours
Mode of Training	Offline / Online	Offline / Online	Offline / Online

Azure Open AI Engineer Certification Course Curriculum

Start your carrer in AI with certification in Azure Open AI Engineer course, that will help in shaping the carrer to the current industry needs that need automation using intelligent workflows like Azure Open AI, Azure AI Foundry, n8n, Microsoft AutoGen, LangGraph & others in every domain & sphere of the industry that will allow organizations to boost decision making & also thrive business growth with improved customer satisfaction.

Course Content

Azure AI Foundry

Course Details - Azure AI Foundry

Intro to AI Agents

Azure AI Foundry Basics

Introduction to Azure AI Foundry
What is Azure AI Foundry?
Architecture of Azure AI Foundry
Projects vs Hubs
How It Differs from Azure OpenAI Service
Navigating the Azure AI Foundry Portal
Model Benchmarks
Access Playgrounds via Foundry
Management Centre

Assistants API – A Refresher

Advice
Introduction to Assistants API
What is Assistants API ?
Assistants API Component / Key Terms
Assistants API Architecture
What is Function Calling ?
Demo: FC: Python Code : Function Calling Intro
Demo: FC: Python Code: Get an API Key from openweathermap.org
Pre-Req Setup azureopenai.env
Demo: FC : Python Code : Initialise and Set the Environment
Demo: FC : Python Code : Instantiate the Client Object of Azure OpenAI
Demo:FC : Python Code: Define the get_weather Function
Demo: FC: Python Code: Define the tools list required for the Assistants API
Demo: FC_ Python Code : Create an Assistant & Thread
Demo: FC:Python Code: Create a function for running the conversation
Demo: FC: Python Code: Take the User input and show result

Azure AI Agent Service

Intro to Azure AI Agent Service
What is Azure AI Agent Service?
Architecture of Azure AI Agent Service
Assistants API Vs Azure AI Agent Service
Model & Region Support
Quotas & Limits
Azure AI Agent Service Pricing
Demo: Create a Hub & Project
Demo: Create an Agent via Azure Foundry
Demo: Understand the various Knowledge & Action Tools
Demo : Get a Response to your prompt via the Agents

Understanding Azure SDK for Python

Introduction to Azure SDK for Python
What is Azure AI Projects client library for Python
Demo: Create VSCode Environment & setup Azure-CLI
Understand the Workflow for Agent Creation
Demo: Create Azure AI Project via Azure AI Foundry
Demo: Create Entra ID Application & Grant Contributor access

Azure AI Agent Service Action Tools – Function Calling

Action Tools - Function Calling
Intro to Function Calling
Demo: Create a new Deployment inside Project
Demo: Understand the Workflow for Weather Agent
Demo : Understand the Environment Variables
Demo : Install Libraries and Setup Environment
Demo: Understand the Get_weather Function
Demo: Calling your Azure AI Agent

Azure AI Agent Service Action Tools – Code Interpreter

Title - Code Interpreter
Intro to Code Interpreter
What is Code Interpreter ?
Understand the Input Data - sales_data.csv
Demo: How to use CI in Agents Playground
Demo: Understand the Workflow for Graph Generator Agent
Demo: Upload the sales_data.csv & Environment variables file
Demo: Understand the Creation of Agent
Demo: Create Thread and Message Conversation
Demo: Run the Agent & Create Graphs

Azure AI Agent Service Action Tools – Open API 3.0

Title - Open API 3.0
What is Open API 3.0 ?
Understanding the Workflow for Yahoo Finance
Demo: Create Rapid API Key
Demo: Create a Connection to Rapid API in Azure
Demo: Create a Stocks Agent and Attach Open API 3.0
Demo: Test the Stocks Agent in Playground
Demo: Install the Libraries and Setup Environment
Authenticate/Initialize/ Load Open API Spec
Get Connection / Create Auth Object / Create Open API Tool
Create Agent / Thread / Conversation Loop
Execute the Program and run Queries

Azure AI Agent Service Knowledge Tools – Bing Search

Title - Knowledge Tools - Bing Search
Intro to Grounding with Bing Search
What is Grounding with Bing Search ?
Demo : Create a Bing Search with Agent Service
Demo: Create a Bing Resource & Create Connection
Demo: Setup environment & install libraries
Demo: Understand the Code
Demo: Execute the Code

Title - Open API 3.0
What is Open API 3.0 ?
Understanding the Workflow for Yahoo Finance
Demo: Create Rapid API Key
Demo: Create a Connection to Rapid API in Azure
Demo: Create a Stocks Agent and Attach Open API 3.0
Demo: Test the Stocks Agent in Playground
Demo: Install the Libraries and Setup Environment
Authenticate/Initialize/ Load Open API Spec
Get Connection / Create Auth Object / Create Open API Tool
Create Agent / Thread / Conversation Loop
Execute the Program and run Queries

Azure AI Agent Service Knowledge Tools – File Search

Title - Knowledge Tools - File Search
Intro to File Search
File Search ?
Demo: Utilize File Search with AI Foundry
Demo: Understand the workflow
Demo: Install Libraries & Setup Environment
Demo: Initialise the Project Client & Upload the File
Demo: Create Vector Store / Agent
Demo: Create Thread and Process Messages
Demo: Run the Code and ask Questions

Azure AI Agent Service Knowledge Tools – Azure AI Search

Title - Knowledge Tools - Azure AI Search
Intro Azure AI Search
What is Azure AI Search ?
Understand the Workflow
Demo: Create a Storage Account & Upload Document
Demo: Ensure / Create you have Azure OpenAI Service
Demo: Create Embeddings Deployment
Demo: Create Azure AI Search Resource
Demo: Create an Index
Demo: Create an Agent & Attach the Knowledge Tool
Demo: Run Queries in Agents Playground
Demo: How to use AI Search in Python Code
Demo: Install Libraries and Setup Environment
Demo: Initialise the Project Client
Demo: Create Agent
Demo: Execute the Code

Real World Scenario – Freshdesk Agent

Title - Freshdesk Agent
Understand the Workflow and what we shall Build
Create a login on Freshdesk & Setup API Key
Install Binaries & Setup Environment
Understand the Custom Function create_freshdesk_ticket
Initialize Azure AI / Create toolset
Create the Freshdesk Agent
Execute the Program and Check the Tickets

Azure AI Foundry Agent Service (Post GA)

Azure AI Foundry Agent Service (Post GA)
Agent Service in GA Mode
Importance of AI Foundry Project
Demo: Create Azure AI Foundry Project
Demo: Create an Agent using Foundry
Azure AI Projects client library for Python b11 Changes
Demo: Create VSCode Environment & Understand Code
Demo: Execute a Simple Code

Azure AI Agent Knowledge Tools - Tripadvisor

Title- Knowledge Tools - Tripadvisor
What is Tripadvisor & Microsoft Partnership
Demo: Create a Connection
Demo : Create Your Tripadvisor Agent
Demo: Execute Prompts against the Agent
Demo: Look into the pre-reqs for the code
Demo: Install Packages and Import Libraries
Demo: Load Environment Variables
Authenticate AI Client & Load Open API Schema
Create open API Tool & Agent
Demo: Create Thread & Handle Conversations
Demo: Execute the Program

Azure AI Agent Service Action Tools – Azure Logic Apps

Title - Azure Logic Apps
What is Azure Logic Apps ?
Demo: Create a Simple Agent and Try Send an Email
Demo: Add Logic App Action
Demo: Test the Agent in the Agents Playground
Title - Coding Send an Email with Logic Apps
Demo: Install / Import Libraries & Set Env
Demo: Understand Send Email Function
Demo: Create Agent
Demo: Process Conversation with Tool Calling
Demo: Understanding the main function
Demo: Execute the Program

Working with the Semantic Kernel SDK (Multi-Agentic Systems)

Introduction to Building Agentic Workflows with Semantic Kernel SDK
What is Semantic Kernel ?
Architecture of Kernel
Understanding Plugins in Semantic Kernel SDK
Various Orchestration Frameworks in Semantic Kernel
Demo: Setup the Project in VS Code
Demo: Import Dependencies & Load Env Variables
Demo: Understand the Functions Defined
Demo: Understand the role of each Agent
Demo: Define Function run_business_post_example
Demo: Understand the main block
Demo: Execute the Agents
Understanding Prompt Template Plugins
Understanding Native Plugins
Architecture for Building a Multi-Agent System
Lab: Building a Multi-Agent System (Hands-On)

Mastering MCP : Beginner to Pro Access the AI Ecosystem

MCP Across AI Frameworks & Platforms : Langchain, LllamaIndex, Open AI SDK, Google ADK
Detailed breakdown of MCP
Claude Desktop and GitHub Copilot on VSCode Installation
Lab 1: Running GitHub MCP Server (Hands-On Demo)
Lab 2: Creating a Weather MCP Server (Hands-On Demo)
Lab 3: Creating an Azure AI Agent Service MCP Server (Hands-On Demo)

Building a Bling Web Searcher Agent

Building a RAG Agent

The What, Why and How of RAG
What Are Vector Embeddings?
Lab: Working with Vector Embedding engine (Hands-On Lab)
Vector Search With Azure Cognitive Search Theory
RAG Agent Architecture
Lab: Creating Our Azure AI Search Index for RAG (Hands-On
Lab: Bringing our RAG agent to life with the SDK (Hands-On Lab)

Semantic Kernel Agent Framework

Introduction to the Semantic Kernel Agent Framework
Lab: Getting Started and creating a "Chat Completions Agent" (Hands-On Lab)
Lab: Creating an "Azure AI Agent" (Hands-On Lab)
Lab: Agent with Native and Prompt Template Plugins (Hands-On Lab)
Lab: Azure AI Agent with Multiple Plugins (Hands-On Lab)
Lab: Agent Group Chat with Azure AI Agents (Hands-On Lab)

Microsoft Agent Framework

Introduction to Microsoft Agent Framework
Lab 1: Creating an AzureOpenAIChat Local Runtime Agent (Hands-On Lab)
Lab 2: Creating an Azure AI Agent with MAF (Hands-On Lab)
Lab 3: Creating Azure AI Agent with MCP Tool (Hands-On Lab)
Lab 4: Multi-Tool Agent with MAF (Hands-On Lab)
Lab 5: Sequential Workflow with MAF (Hands-On Lab)
Lab 6: Parallel Workflow with MAF (Hands-On Lab)
Lab 9: Conditional Workflow with MAF (Hands-On Lab)

AI Foundry Local : Local LLM’s for the win

Introduction to AI Foundry Local
Lab: Onboarding AI Foundry Local to our Device (Hands-On Lab)
Lab: Calling Foundry Local Models via Python Notebook (Hands-On Lab)
Lab: Getting Started with Langchain and AI Foundry Local (Hands-On Lab)
Lab: Web UI for AI Foundry Local (Hands-On Lab)

Evaluating Our Agent

Introduction to GenAI Evaluation and AI Evaluation SDK
Lab1: Getting Started with the AI Evaluation SDK (Hands-On Lab)
Mechanics of the AI Evaluation SDK: How it Works
Lab: Evaluating our RAG Agent with Groundedness (Hands-On Lab)
Lab: Building a Custom Prompt Evaluator
Introduction to Agent Tracing Capability
Lab: Connecting Application Insights to Azure AI Project (Hands-On Lab)
Lab: Tracing our Agent via Code (Hands-On Lab)
Lab: Building Token Usage Dashboards for Cost Optimization (Hands-On Lab)

Azure AI Content Understanding – Multi-Modal RAG

Introduction to Azure AI Content Understanding
Lab: Trying out Content Understanding in Azure Portal (Hands-On Lab)
Lab: Content Understanding with Python Notebook (Hands-On
Intro to Sustainability RAG Project Architecture
Lab: Data Preparation for RAG (Hands-On Lab)
Lab: Building and Running the RAG Pipeline (Hands-On Lab)

Capstone Project 1 : Building a Search Engineer with Vector Embeddings

Project Introduction: The Why, What and How?
Introduction to RAG with Azure Cosmos DB
RAG with Cosmos DB for NoSQL API: Architecture
RAG with Cosmos DB for NoSQL API: Hands on Lab
Deploying Resources on Azure (Hands-On Lab)
Running our Project (Hands-On Lab)

Capstone Project 2 : Building a Video Generation with Multi-Agent System

Build , Deploy and Monitor Multi-Agents Applications using Azure AI Foundry . These tools helps to build a multi-agentic using Foundry Models, Foundry Agent Service, Foundry Observability & others then visualize them on customized dashboards in form of graphs & bar charts. We can also bring automation using these Agentic AI platform that will help to automate business processes autonomous AI Agents .

Open AI & Azure Open AI

Course Details - Open AI & Azure Open AI

Azure Open AI Foundations

Azure OpenAI - Intro
What is Azure OpenAI
History behind Azure OpenAI
Models available with Azure OpenAI(Regions)
Limits & Quotas - Important Consideration
How Pricing Works in Azure OpenAI
Demo: Setup Azure OpenAI Service
What is Azure Open AI Studio ?
Demo: Azure OpenAI Studio Walkthrough
Chat Playground - Demo: Create a Deployment of Chats Playground
Understand the Chat Playground
Demo: Deploy a Webapp from the Playground
DALL-E Playground - Demo on Generating Images
What is Completions Playground ?
Demo: Completions Playground

Azure AI Foundry

What is Azure AI Foundry ?
What is Azure AI Foundry?
Architecture of Azure AI Foundry
Projects vs Hubs
How It Differs from Azure OpenAI Service
Navigating the Azure AI Foundry Portal
Model Benchmarks
Access Playgrounds via Foundry
Management Centre

Azure Open AI – Making API Calls

Title - Azure OpenAI - Making API Calls
API Calls - Intro
OpenAI API Calls Vs Azure OpenAI API Calls
Demo: Create a New Azure OpenAI Service
Demo: Get the Values of Endpoint URL & API Keys
Demo: Create an azureopenai.env File
Demo: Get the value of api_version
Demo: Create a New Deployment of Chats Completion
Demo: Make a Simple Azure OpenAI API Call

Azure Open AI – RAG with Azure AI Search

Title - BYOD
RAG - Introduction
What is Azure AI Search
How Vector Search Works with Azure AI Search
Understanding the ACAS Data
Pre-reqs for RAG with Azure AI Search
Create a Storage Account
Create an Embedding Deployment
Create a chats deployment
Create Azure AI Search Resource
Uploads Documents to Chats Deployment
Perform Queries - Using your Own Data

Azure Open AI - Fine Tuning

Azure OpenAI Fine Tuning
Azure OpenAI Fine Tuning- Introduction
What is Fine Tuning ?
Understanding of Fine-Tuning Regions & Models
Use Cases of Fine Tuning
Demo: Create Azure OpenAI Service in US East2
Demo: Prepare & Upload data
Demo: Create a Fine-Tuning Job
Demo: Evaluate Fine Tuning Model
Demo: Deploy the Fine-Tuned Model
Demo: Query the Fine-Tuned Model-Part1
Demo: Query Fine Tuned Model - Part 2
Fine Tuning with Python Code
Demo: FT : Python Code : Understand the Environment
Demo: FT: Python Code: Upload the Training & Validation Dataset to Azure OpenAI
Demo: FT : Python Code: Start the Fine-Tuning Job
Demo: FT : Python Code: Monitor the Fine-Tuning Job
Demo: FT: Python Code: Create a Deployment based on Fine Tuned Model
Demo: FT : Python Code : Query against your Deployment based on Fine Tuned Model

Azure Open AI - Content Filtering

Title- Azure OpenAI Content Filtering
Content Filtering - Introduction
What is Content Filtering ?
Categories Covered under Content Filtering
What are Prompt Shields ?
Demo: Run a Query in Chats without Content Filtering
Demo: Create a Custom Filter
Demo: Apply Content Filter to Deployment
Demo: See the impact of Content Filtering

Azure Open AI - Identity & Access Control (IAM)

Title- Azure OpenAI Access Control
IAM / RBAC - Introduction
What is Azure RBAC Model ?
RBAC for Azure OpenAI
Demo: Login to Azure OpenAI Service and Check Permissions
Demo: Create a new User in Azure
Demo: Login with the new User and check resources
Demo: Perform Role Assignment based on Cognitive Roles
Demo: Refresh and check the access

Assistants API – Intro & Code Interpreter

Introduction to Assistants API
What is Assistants API ?
Assistants API Component / Key Terms
Assistants API Architecture
Demo: AAPI: Python Code: Introduction (Maths Tutor)
Demo: AAPI: Python Code: Initialise Environment
Demo: AAPI:Python Code: Create Client & Assistant Objects
Demo: AAPI: Python Code:Create a Thread
Demo: AAPI: Python Code: Send Messages from User to Thread
Demo:AAPI: Run Assistant & Display the Messages back to the user
Demo: Run like a ChatBot (in a while Loop)
What is Code Interpreter ?
Demo: Analysing the Code and checking what it does
Demo: Code Interpreter - Making Code Fixes
Demo: Code Interpreter - Uploading the Failed_Banks Data
Demo: Code Interpreter - Query the Data
Demo:CI: Python Code: Upload a file to the Azure OpenAI service
Demo:Python Code: Create an Assistant

Assistants API – Function Calling

Assistants API - Function Calling - Intro
What is Function Calling ?
Demo: FC: Python Code : Function Calling Intro
Demo: FC: Python Code: Get an API Key from openweathermap.org
Demo: FC : Python Code : Initialise and Set the Environment
Demo: FC : Python Code : Instantiate the Client Object of Azure OpenAI
Demo:FC : Python Code: Define the get_weather Function
Demo: FC: Python Code: Define the tools list required for the Assistants API
Demo: FC_ Python Code : Create an Assistant & Thread
Demo: FC:Python Code: Create a function for running the conversation
Demo: FC: Python Code: Take the User input and show result

Title - Open API 3.0
What is Open API 3.0 ?
Understanding the Workflow for Yahoo Finance
Demo: Create Rapid API Key
Demo: Create a Connection to Rapid API in Azure
Demo: Create a Stocks Agent and Attach Open API 3.0
Demo: Test the Stocks Agent in Playground
Demo: Install the Libraries and Setup Environment
Authenticate/Initialize/ Load Open API Spec
Get Connection / Create Auth Object / Create Open API Tool
Create Agent / Thread / Conversation Loop
Execute the Program and run Queries

Assistants API – File Search

Assistants API - Intro
What is File Search ?
Demo: Look into Pre-Reqs and File to be uploaded
Demo: Create a Deployment & Assistants API Resource
Demo: Configure the Assistants API & Upload the File
Demo: Run Queries against the Uploaded Document
Demo: Understand the Flow of the Python Program
Demo: Code : Initialise and Set the Environment
Demo: Code : Create a new Assistant with File Search Enabled
Demo:Code : Upload files and create a vector store
Demo:Code:Attach a Vector Store to Assistant
Demo : Code :Upload File and Create a Thread
Demo: Code: Main loop to handle Questions

[RAG] – Azure AI Search – Azure Open AI - LangChain

Title: Azure AI Search - Azure OpenAI-LangChain
Understanding the workflow
Demo: Look into Pre-Reqs and File to be uploaded
Demo: Install Libraries and Setup Environment
Demo: Create a new Azure Table based on Uploaded File
Demo: Create Azure AI Search Service & Indexer
Demo: Update your environment variables
Demo: Initialize the Retriever, Prompt, and LLM (langchain in action)
Demo: Processing Chain and User Input Loop

AI Agents

Title - Azure AI Agents
What is Agentic AI ?
Assistants API vs Azure AI Agents
Understand the Workflow for Agent Creation
Demo: Create Azure AI Project via Azure AI Foundry
Demo: Create a new Deployment inside Project
Demo: Create Entra ID Application & Grant Contributor
Demo: Understand the Workflow for Weather Agent
Demo : Understand the Environment Variables
Demo : Install Libraries and Setup Environment
Demo: Understand the Get_weather Function
Demo: Calling your Azure AI Agent

Open AI Agents SDK & MCP

Title - OpenAI Agents SDK & MCP
Full Course on MCP
What is MCP ?
Trending on GitHub stars
What is OpenAI Agents SDK
What is Azure MCP Server
Understand the Workflow
Demo: Work on the Pre-Reqs
Demo: Project Setup & Install Packages
Demo: Check Environment File & Ensure az cli works
Demo: Understanding the Code
Demo: Running the Code

Semantic Kernel

Title : Semantic Kernel
What is Semantic Kernel ?
Architecture of Kernel
What is Plugin ?
Various Orchestration Frameworks in Semantic Kernel
What are we going to build ?
Demo: Setup the Project in VS Code
Demo: Import Dependencies & Load Env Variables
Demo: Understand the Functions Defined
Demo: Understand the role of each Agent
Demo: Define Function run_business_post_example
Demo: Understand the main block
Demo: Execute the Agents

Build , Deploy and Monitor Multi-Agents Applications using Azure Open AI . These tools helps to build a multi-agentic using Foundry Models, Foundry Agent Service, Foundry Observability & others then visualize them on customized dashboards in form of graphs & bar charts. We can also bring automation using these Agentic AI platform that will help to automate business processes autonomous AI Agents .

Capstone Project

Gain practical, real-world experience

Engineered by renowned industry specialists

Project 1

Real-Estate Lead Enhancement System

Build a Real Estate Lead Enhancement System, covering all two main workflows. The system processes real estate leads through AI-powered behavioral analysis, market intelligence gathering, intelligent agent routing, and automated communication.

Project Description :
• Workflow 1: Agent/Client onboarding and synchronization between Google Sheets and Salesforce. This workflow handles the synchronization of agent and client data between Google Sheets and Salesforce, typically triggered manually when new agents or clients are onboarded.
• Workflow 2: Main lead processing pipeline with three distinct phases
This workflow handles property search, market research, intelligence analysis, and intelligent agent routing based on geography and specialization.
Business Logic: Pass-through merge node that forwards consolidated property data to market intelligence processing pipeline. Acts as workflow transition point between property acquisition and analysis phases.

Project 2

Travel Management System

Build a Travel Management System, consists of multiple Agents working collaboratively to create a successful Trip Plan when asked by the user. The system processes the user prompt when user asked “Book me a trip for the month of December for Paris or something like that

Project Description :
• Workflow : Agent/Client onboarding and synchronization between Google Sheets and Salesforce. This workflow handles the synchronization of agent and client data between Google Sheets and Salesforce, typically triggered manually when new agents or clients are onboarded.
• Workflow 2: It consist of multiple Agents, one is the Planner Agent that will pal the trip & create the travel itineraries like hotel information, optimized route plan, hotel amenities, weather information & other details. The next agent Booking Agent handle the booking queries once Planning Agent retrieve all the information & there will be a HITL(Human in the Loop) before actually booking is done by the system.

80+

Hours of content

24+

Live Sessions

Software Tools

Request More Information

Tools Covered

Azure Open AI Course Certification

After completion of this training program you will be able to launch your carrer in the world of AI being certified as Azure AI Foundry Certified Professional.

With the AI Certification in-hand you can boost your profile on Linked, Meta, Twitter & other platform to boost your visibility

Gen AI Skills to Highlight on Your Resume

Career Outcomes

Our Alumni in Top Companies

Career help alongside Azure Open AI / Azure AI Foundry Course

What our audience are saying in their Testimonials

The course gave me hands-on experience with Generative AI pipelines and how vector databases optimize LLM performance

I can build scalable GenAI systems with RAG

Course explain through RAG system design, vector database architecture, and deployment strategies

Azure Open AI Engineer Course FAQs

Why should you choose to pursue a Azure Open AI/Azure AI Foundry course with Success Aimers?

Success Aimers teaching strategy follow a methodology where in we believe in realtime job scenarios that covers industry use-cases & this will help in building the carrer in the field of AI & also delivers training with help of leading industry experts that helps students to confidently answers questions confidently & excel projects as well while working in a real-world

What is the time frame to become competent as a Azure Open AI engineer?

To become a successful Azure Open AI/Azure AI Foundry Engineer required 1-2 years of consistent learning with dedicated 3-4 hours on daily basis.
But with Success Aimers with the help of leading industry experts & specialized trainers you able to achieve that degree of mastery in 6 months or one year or so and it’s because our curriculum & labs we had formed with hands-on projects.

Will skipping a session prevent me from completing the course?

Missing a live session doesn’t impact your training because we have the live recorded session that’s students can refer later.

What industries lead in Azure Open AI/Azure AI Foundry implementation?

Manufacturing
Financial Services
Healthcare
E-commerce
Telecommunications
BFSI (Banking, Finance & Insurance)
Travel Industry

Does Success Aimers offer corporate training solutions?

At Success Aimers, we have tied up with 500 + Corporate Partners to support their talent development through online training. Our corporate training programme delivers training based on industry use-cases & focused on ever-evolving tech space.

How is the Success Aimers Azure Open AI/Azure AI Foundry Certification Course reviewed by learners?

Our Azure Open AI/Azure AI Foundry Engineer Course features a well-designed curriculum frameworks focused on delivering training based on industry needs & aligned on ever-changing evolving needs of today’s workforce due to AI.
Also our training curriculum has been reviewed by alumni & praises the thorough content & real along practical use-cases that we covered during the training. Our program helps working professionals to upgrade their skills & help them grow further in their roles…

Can I attend a demo session before I enroll?

Yes, we offer one-to-one discussion before the training and also schedule one demo session to have a gist of trainer teaching style & also the students have questions around training programme placements & job growth after training completion.

What batch size do you consider for the course?

On an average we keep 5-10 students in a batch to have a interactive session & this way trainer can focus on each individual instead of having a large group.

Do you offer learning content as part of the program?

Students are provided with training content wherein the trainer share the Code Snippets, PPT Materials along with recordings of all the batches

Similar Courses

Agentic AI Frameworks and Workflow Automation Course in Gurgaon

Agentic AI Frameworks and Workflow Automation Course in Gurgaon AI is the future innovation with...

Agentic AI with Lang Chain and Lang Graph Training Course in Gurgaon

Agentic AI with Lang Chain and Lang Graph Training Course in Gurgaon With high-demand of...

AI Agent and Agentic AI Workflows with N8N Course in Gurgaon

AI Agent and Agentic AI Workflows with N8N Course in Gurgaon Master the future of...

AI Infrastructure Scaling and Governance with MCP Certification Course in Gurgaon

AI Infrastructure Scaling and Governance with MCP Certification Course in Gurgaon AI is the future...

AWS Bedrock Training Course in Gurgaon

AWS Bedrock Training Course in Gurgaon AI is the future innovation with Agentic AI tools...

Gen AI Course for Software Testing in Gurgaon: Automation and Quality

Gen AI Course for Software Testing in Gurgaon: Automation and Quality AI is the future...

Generative AI Certification Course in Gurgaon

Generative AI Certification Course in Gurgaon AI is the future innovation with Agentic AI tools...

Generative AI Course for Business Leaders and Senior Management in Gurgaon

Generative AI Course for Business Leaders and Senior Management in Gurgaon Kickstart your carrer in...

Generative AI Course for Cyber Security Professionals in Gurgaon

Generative AI Course for Cyber Security Professionals in Gurgaon AI is the future innovation with...

Generative AI Course for Software Development and Testing in Gurgaon

Generative AI Course for Software Development and Testing in Gurgaon AI is the future innovation...

Generative AI with Vector Databases and RAG Certification Course in Gurgaon

Generative AI with Vector Databases and RAG Certification Course in Gurgaon AI is the future...

Google Cloud AI Training Course in Gurgaon

Google Cloud AI Training Course in Gurgaon AI is the future innovation with Agentic AI...

OpenAI ChatGPT Training Course in Gurgaon

OpenAI ChatGPT Training Course in Gurgaon AI is the future innovation with Agentic AI tools...

Azure Open AI & Azure AI Foundry Training Course

Azure Open AI & Azure AI Foundry Course

Azure Open AI Engineer Certification Course Curriculum

Download Curriculum

Book Free Demo Session

Corporate Training

Let's Connect to Discuss

Enquire Now

Categories

WhatsApp Contact

Call

Connect to Us