Description

What is Azure AI Engineer Certification Training?

Azure AI Engineer Certification Training is a professional training program designed to teach individuals how to build, manage, and deploy artificial intelligence (AI) solutions using Microsoft Azure’s AI tools and services.

It prepares learners for both practical industry work and the Microsoft Certified: Azure AI Engineer Associate (AI-102) certification exam.

Industries Hiring Azure AI Engineer Talent:

Technology & Software / Cloud Services

Core use of cloud platforms + AI tools. They build platforms, frameworks, and services that require scalable AI / ML infrastructure.

Healthcare & Life Sciences / Pharmaceuticals

For medical imaging, diagnostics, genomics, drug discovery, patient-data analysis, personalized medicine. They need scalable, compliant cloud AI services.

Financial Services / Banking / Insurance / FinTech

For fraud detection, risk modeling, algorithmic trading, customer experience (chatbots, virtual assistants), compliance with heavy data loads. AI/ML models + secure cloud deployment are critical.

Retail / E-Commerce

To provide personalized recommendations, inventory/forecasting, supply chain optimization, chatbots for customer service, image recognition, demand prediction.

Manufacturing / Industrial IoT

Predictive maintenance, quality control (using computer vision), optimizing production workflows, integrating sensors/IoT data, automating inspections.

Telecommunications

Networks optimization, customer support bots, natural language processing for call centers, anomaly detection (network intrusion, faults), managing large-scale data streams.

Consulting / Professional Services

Many firms advise/implement AI/cloud solutions for their clients; they need engineers with Azure AI skills for projects across many domains.

Government / Defense / Public Sector

For projects related to national security, infrastructure, public health, disaster response; compliance, data-privacy/security are big; many agencies now using Azure for cloud+AI.

Media, Entertainment & Gaming

Personalization of content, content tagging, image/video processing, moderation, recommendation, virtual assistants / voice control.

Why Azure Specifically Is Attractive in These Industries

Scalability & Global Infrastructure:

Azure offers tools and data centers globally, which many of these industries need.

Comprehensive AI/ML Toolset:

Azure has Cognitive Services, Azure Machine Learning, OpenAI integration, etc., which make building AI solutions easier.

Security, Compliance & Data Governance:

Industries like healthcare, finance, government demand strong compliance, auditing, privacy protections — which Azure provides (e.g. compliance certifications, region-specific data centers).

Hybrid / Edge Capabilities:

Many industries (manufacturing, energy, IoT) need edge computing, hybrid clouds; Azure supports that.

Ecosystem & Integration:

Integration with other Microsoft tools (Power BI, Office, Dynamics, Teams) is a big plus for enterprises already invested in Microsoft stacks.

Job Market Trends for Azure AI Engineers

Strong Demand, Growing Fast

- More companies are adopting AI, machine learning, and cognitive services in production, which increases demand for engineers who can build, deploy, and maintain AI solutions on platforms like Azure. The demand Azure AI Engineers talent has led to competitive salaries. According to recent estimates, salaries in the U.S. range from $130,000 to over $150,000 annually, with senior and specialized roles commanding higher compensation.

The following topics will be covered as part of Azure AI Engineer Associate Certification Training

Introduction Azure AI Solution

Section Intro

Understanding of ML

Generative AI intro

Start with ChatGPT

LLMs intro

GPT intro

OpenAI

Understanding Model Lineup

What is multi modal LLM

OpenAI API Platform signup

OpenAI API

Using the playground and system messages

Intro to Azure

Signing up for Azure free account

Tour of Azure Portal

Azure AI Services

AI Safety and Responsibility

Implementing Generative AI Solutions

Azure Open AI and Azure Foundry
Deploying Azure AI foundry resource – Overview
Lab – Creating Azure AI foundry
Launching Azure AI foundry portal
Azure AI Foundry – Exploring the models
Azure AI Foundry – Deploying a model – overview
Lab – Azure AI Foundry – Deploying a model
Lab – Azure AI Foundry – Using playground
Lab – Azure AI Foundry – Model Parameters
Lab – Azure AI Foundry – System Prompt
Lab – Azure AI Foundry – Image Generation
Lab – Azure AI Foundry – Basic Prompt fundamentals
Prompt Engg – Basic Prompt fundamentals
Prompt Engg – Zero shot prompting and few shot promptimg
Prompt Engg – Chain of thought prompting
Lab – Installing Python and VSCode
Lab – Running a simple Python code
Lab – GPT5 – REST API
Lab – Python GPT5 Chat Completion API
Lab – Python GPT5 System messages and Getting Entire response
Monitoring the model usage in Azure
Lab – Python GPT5 – Asking model to explain the code
Lab – Python – Using model to generate an image
Fine tune Azure OpenAI Models
Overview of Finetuning a model
Retrieval Augmented Generation ( RAG )
Lab – RAG Azure Blob Service
Lab – RAG Implementation
Lab – RAG Using Python
Azure AI Content Safety
Lab – Azure AI Content Safety Adding a content filter
Azure AI Content Safety Block list
Lab – Azure AI Content Safety Resource
Lab – Azure AI Content safety Studio – Prompt Shield
Lab – Python – Azure AI Content Safety as a service – detect harmful text
Lab – Azure AI Content Safety – Making reST API calls
Lab – OpenAI Using new response API
Lab – OpenAI Streaming the response
Lab – OpenAI Understanding structured Outputs
Lab – OpenAI Extracting details as text
Lab – OpenAI – Structured output –
Lab – OpenAI – Structured output Using chat completions API
OpenAI Concept of Using Tools
Open AI Tools – Image generation and WebSearch

Implementing Computer Vision Solutions

Section Intro
Lab – Creating a computer vision resource
Lab – Computer Vision – Image tagging
Lab – Computer Vision – Python – image tagging
Lab – Computer Vision – Python Generate Caption
Lab – Computer Vision – Python – Object Detection
Lab – Computer Vision – Python OCI
Lab – VScode – making API calls
Lab – Computer Vision – REST API Calls – GEnerate Caption and other features
Face Service
Lab – Face Service – Python
Azure AI Custom Vision
Lab – Azure AI Custom Vision Create the Azure resources
Lab – Azure AI Custom Vision IMage classification model
Quick understanding on training results
Lab – Azure AI Custom Vision – Python – Image classification and Image Classification
Azure AI Custom vision – Object detection model
Azure AI Video indexer
Lab – Azure AI video indexer
Azure AI video indexer – Embedding widgets
Azure AI Video indexer – Customize content models

Implement NLP Solutions

Section Intro
Lab – Azure AI Language – Detect language
Lab – Azure AI Language resource
Lab – Azure AI Language – Detect language – API call
Lab – Azure AI Language -Detect language – Python
Lab – Azure AI Language – Extract key phrases
Lab – Azure AI Language – Extract key phrases – Python
Lab – Azure AI Language – Analyze Sentiments and Using python
Lab – Azure AI Language – Extract Entities and using python
Lab – Azure AI Language – Summarization Service
Lab – Azure AI Language – PII
Azure AI translator Service
Lab – Azure AI translator – Python and API call
Azure Speech Service and Lab
Lab – Azure Speech Service – Text to Speech and using Python
Lab – Speech Synthesis Markup language
Lab – Azure Speech – Speech to text and Using python ( Local file )
Lab – Azure Speech – SPeech translation service
Conversational Language Understanding – Intro
Lab – getting started with language studio
Lab – Conversational Language Understanding – Adding intents and entities
Lab – Conversational Language Understanding – Adding utterances
Lab – Conversational Language Understanding – Training and deploying the model
Lab – Python – Consume Conversational Language Mode
Azure Conversational Language understanding – Best practices
Conversational Language Project – Backup
Custom question answering – Intro
Lab – Custom question answering – Getting Started
Lab – Custom question answering – Edit knowledge Base
Lab – Custom question answering – Deploy and use knowledge base
Lab – Creating a bot

Implementing Knowledge Mining and Information Extraction Solutions

Section Intro
Lab – Creating document intel service
Lab – Azure AI Doc intelligence service – Read model
Lab – Azure AI Doc intelligence service – Read model – Python
Lab – Azure AI Doc intelligence service – Read model implementation
Lab – Azure AI Doc intelligence service – Invoice model and using python
Lab – Azure AI Doc intelligence service – Receipt Model
Lab – Azure AI Doc intelligence service – Testing for handwritten text
Lab – Python – Using combination of services
Lab – Azure AI Doc intelligence – REST API calls and Custom models
Lab – Azure AI Doc intelligence service – Custom Model – Label data
Lab – Azure AI Doc intelligence service – Custom model – train and use the model
Azure AI Doc intelligence – Input requirements and limit
What are ARM templates?
Lab – Azure ARM template – Azure AI Doc intelligence
Lab – Create Azure AI Services resource – API call – pre requ –
Lab – Create Azure AI Servicesresource – REST API implementation
What is Azure AI Content understanding
Azure AI content understanding – Extract text from docs and audio analysis
Azure AI content understanding – Creating custom analyzer
What is Azure AI Search
Lab – Creating search index
Lab – Azure AI Search – Using search index and creating skillset
Azure AI Search knowledge store
Lab – Azure AI Search knowledge store
Lab – Azure AI Search using key