Azure AI Training in Hyderabad Built on AI-103, not the retired AI-102
Azure AI training in Hyderabad is instructor-led training in building AI applications and agents on Microsoft Foundry — generative models, retrieval-augmented generation, agents, computer vision and information extraction. AzureTrainings runs classroom batches in KPHB, Kukatpally and live online batches, structured around AI-103 (Developing AI Apps and Agents on Azure), the exam that replaced AI-102 when it retired on 30 June 2026.
Course snapshot
Azure AI Training in Hyderabad — Quick Facts
Everything a prospective learner asks a counsellor in the first two minutes.
Course
Azure AI Training
Location
JNTU / KPHB, Hyderabad
Training mode
Classroom + live online
Duration
3 months
Certification target
AI-103: Developing AI Apps and Agents on Azure
Replaces
AI-102, retired 30 June 2026
Real-time projects
5 projects on Microsoft Foundry
Key services
Microsoft Foundry, Azure OpenAI, Agent Service, AI Search, Content Understanding
Language support
English, Telugu and Hindi
Prerequisites
Python. Taught from scratch in Module 2 if you need it.
Placement support and career outcomes
What we do, stated plainly, and what we do not claim.
What we actually do
Resume and LinkedIn rebuilt around AI work you have shipped, mock interviews with the trainer, and preparation on the questions this role is actually asked.
What we do not claim
We do not quote a placement percentage and we do not guarantee a job. No institute can evidence either.
Who hires these skills
Hyderabad's capability centres are staffing generative AI and agent teams. A working RAG pipeline and a deployed agent is what gets you shortlisted, not a certificate alone.
Companies hiring Azure AI professionals in Hyderabad include the firms below. They are not partners of, or affiliated with, Azure Trainings.
TCS
Infosys
Wipro
Accenture
Cognizant
Capgemini
Deloitte
Tech Mahindra
Why choose this Azure AI institute in Hyderabad
Every reason below is something you can verify before you pay.
Built on AI-103, not AI-102
AI-102 and the Azure AI Engineer Associate credential retired on 30 June 2026. Most syllabuses you will find online still teach the retired exam. This one does not.
Agents are a first-class topic
AI-103 puts generative AI and agentic solutions at 30–35% — the largest section. We spend accordingly, including multi-agent orchestration and approval flows.
Python from scratch if needed
AI-103 assumes you can develop in Python. If you cannot, Module 2 covers it before any AI service is introduced.
Responsible AI is taught, not mentioned
Content filters, prompt shields, groundedness evaluation and trace logging are exam topics and production requirements. They get their own module.
Cost and quota are covered
Tokens, quotas, rate limits and scaling. The part that decides whether your project survives contact with a budget.
A named trainer
One trainer for the batch, named on this page, with a public LinkedIn profile you can check before you enrol.
Azure AI Course Syllabus — 14 Modules
Mapped to the five AI-103 skill areas as Microsoft publishes them.
01 AI on Azure: the current landscape
You will cover: What Microsoft Foundry is, and what it replaced · Foundry hubs, projects and resources · Choosing between LLMs, small models and multimodal models · Where AI-102 ended and AI-103 begins · Setting up your own Foundry project
02 Python for AI engineering
You will cover: Environments, packages and notebooks · Requests, async and error handling · Working with JSON and structured output · SDK patterns you will reuse all course · Taught from scratch — skip if you already code
03 Deploying and consuming models
You will cover: Choosing an appropriate model per task · Deployment options and configuration · Endpoints, keys and keyless credentials · Foundry SDKs and connectors · Connecting an application to a Foundry project
04 Prompting and generation control
You will cover: Prompt engineering techniques that survive review · Parameters that control generative behaviour · Prompt templates and reuse · Structured and JSON outputs · Where prompting stops and retrieval starts
05 Retrieval-augmented generation
You will cover: Why grounding beats fine-tuning for most problems · Ingesting and indexing documents, images, audio and video · Semantic, hybrid and vector search · Chunking, embeddings and relevance · Building a RAG pipeline end to end
06 Azure AI Search in depth
You will cover: Provisioning, indexes and skillsets · Data sources and indexers · Custom skills · Query syntax, filtering and sorting · Vector and semantic configuration
07 Building agents
You will cover: What an agent is, and when you do not need one · Agent roles, goals and tool schemas · Function calling and conversation memory · Foundry Agent Service · Testing and deploying an agent
08 Multi-agent orchestration
You will cover: Orchestrating several agents · Autonomous versus semi-autonomous workflows · Approval flows and safeguards · Error analysis on agent behaviour · When orchestration is the wrong answer
09 Evaluation and observability
You will cover: Evaluating models and apps for relevance and quality · Detecting fabrications and ungrounded answers · Tracing, token analytics and latency breakdowns · Model reflection and self-critique loops · Collecting and acting on feedback
10 Responsible AI and safety
You will cover: Safety filters, guardrails and content moderation · Prompt shields and indirect prompt injection · Risk detection and blocklists · Auditing: trace logging, provenance, approval workflows · Governing agent behaviour and tool access
11 Computer vision and multimodal
You will cover: Image and video generation from prompts · Inpainting, masked edits and prompt-driven modification · Captioning, alt-text and visual question answering · Content Understanding for visual characteristics · Object and region identification
12 Text, speech and translation
You will cover: Entities, topics, summaries and structured extraction · Sentiment, tone and sensitive content detection · Speech to text and text to speech for agents · Custom speech models and multimodal audio reasoning · Translation with Translator and LLM flows
13 Information extraction from documents
You will cover: OCR, layout analysis and field extraction · Content Understanding analysers · Markdown and structured output for downstream reasoning · Connecting extraction to agent tools · Quality and grounding checks
14 Operations, cost and the exam
You will cover: Quotas, scaling, rate limits and cost footprint · Managed identity, private networking and role policies · CI/CD for Foundry projects · AI-103 exam-style scenario practice · Resume review and mock interviews
Azure AI services you will actually use
Each service, what it does, and where it shows up in the modules and projects.
Service
Purpose
Where you use it
Microsoft Foundry
The hub, project and deployment layer for AI on Azure
Every module from 1 onward
Azure OpenAI in Foundry Models
LLMs, multimodal and image models
Modules 3, 4, 11
Foundry Agent Service
Building and deploying agents
Modules 7 and 8
Agent Framework
Complex and multi-agent workflows
Module 8, project 3
Azure AI Search
Indexing, vector and semantic search
Modules 5 and 6
Content Understanding
Document, image, video and audio extraction
Modules 11 and 13
Document Intelligence
OCR, layout and field extraction
Module 13, project 4
Azure Speech
Speech to text, text to speech, custom models
Module 12
Azure Translator
Text and document translation
Module 12
Content Safety
Filters, blocklists, prompt shields
Module 10, all projects
Evaluation tooling
Groundedness, relevance and safety evaluators
Module 9
Python SDKs
How you actually build all of the above
Modules 2 onward
Azure AI skills you will learn
Ten concrete capabilities you walk out with.
Choose the right model
Match LLM, small model or multimodal to the task, and justify the cost.
Ship a grounded RAG pipeline
Ingest, chunk, embed, retrieve and answer — with citations that hold up.
Build an agent that does work
Tools, function calling and memory, with approval flow where it matters.
Orchestrate multiple agents
And recognise the many cases where one agent is the better design.
Prove the output is grounded
Evaluate for fabrication, relevance and safety instead of eyeballing it.
Instrument what you deploy
Tracing, token analytics, latency and safety signals from day one.
Apply responsible AI properly
Filters, prompt shields, blocklists, provenance and audit trails.
Control cost
Quotas, rate limits, scaling and token economics before finance asks.
Extract from real documents
OCR, layout and field extraction into structured output agents can use.
Secure an AI workload
Managed identity, keyless credentials, private networking, role policies.
Real-time Azure AI projects you will build
Five builds on Microsoft Foundry. Each one produces something you can open in an interview.
Grounded document assistant
Ingest a document set into Azure AI Search, build a RAG pipeline with hybrid and vector retrieval, and answer questions with citations. Evaluate for groundedness and fabrication.
Customer support agent
Build an agent with function calling against a mock ticketing API, conversation memory, and an approval step before any write action.
Multi-agent research workflow
Orchestrate a retrieval agent, an analysis agent and a drafting agent, with tracing across the whole run and error analysis on failures.
Invoice extraction pipeline
Use Document Intelligence and Content Understanding to extract fields from scanned invoices into structured JSON, then expose it as an agent tool.
Safety and governance layer
Add content filters, blocklists and prompt-shield protection to an existing app, with trace logging and provenance metadata for audit.
Who can join this Azure AI training?
One requirement: you can code, or you are willing to learn Python in Module 2.
Python developers
The shortest route. You have the language; the course adds the AI services and the patterns.
Azure engineers
You know the platform. This adds Foundry, models, agents and the responsible AI layer.
Data engineers and analysts
You already move data. RAG and extraction are the natural extension.
Backend and full-stack developers
Agents and RAG are application work. Your integration skills transfer directly.
Solution architects
The planning and management section is 25–30% of the exam and most of your job.
Career switchers with coding basics
Python is taught in Module 2. Bring the willingness to debug.
Azure AI certification in Hyderabad: AI-103, not AI-102
AI-102 and the Azure AI Engineer Associate credential retired on 30 June 2026.
AI-102
Azure AI Engineer Associate
• Exam can no longer be scheduled
• Certification is no longer issued
• Renewal assessment withdrawn
• Still taught by many syllabuses online
AI-103
Developing AI Apps and Agents on Azure
• Skills measured as of 16 April 2026
• Built on Microsoft Foundry
• Passing score 700
• Assumes Python development experience
AI-103 skills measured, with Microsoft’s weightings
Implement generative AI and agentic solutions
30–35% of the exam
Generative apps, RAG, agents, multi-agent orchestration, optimisation and observability. The largest section.
Plan and manage an Azure AI solution
25–30% of the exam
Choosing services and models, deployment, security, monitoring, cost, and responsible AI governance.
Implement computer vision solutions
10–15% of the exam
Image and video generation and editing, multimodal understanding, responsible AI for visual content.
Implement text analysis solutions
10–15% of the exam
Entities, sentiment, summarisation, translation, and speech as an agent modality.
Implement information extraction solutions
10–15% of the exam
Retrieval and grounding pipelines, and extracting content from documents.
AI-102 versus AI-103
AI-102
AI-103 (this course)
Status
Retired 30 June 2026
Current
Exam title
Designing and Implementing a Microsoft Azure AI Solution
Developing AI Apps and Agents on Azure
Can you sit it?
No — exam withdrawn and credential no longer issued
Yes
Agents
Small section added late
30–35% combined with generative AI
Platform naming
Azure AI Foundry
Microsoft Foundry
Taught here
Covered only as history
The whole course
Weightings come from Microsoft’s AI-103 study guide. Check it before you book, and check the exam fee there too rather than trust a number from any institute, including this one.
Your trainer
Every batch is taught live by a named trainer you can look up before you enrol.
Bharat Sriram, lead trainerBharat Sriram
Azure Cloud Architect · Lead Trainer · 15+ years
Bharat has spent 15+ years building cloud and AI systems for enterprise teams before teaching them, and has delivered Azure projects across Hyderabad, Bangalore and US-based engagements since 2010. His sessions run in the portal and the editor rather than on slides, with deep expertise in Microsoft Foundry, retrieval-augmented generation and agent design.
15+
Years industry experience
1,329+
Students trained since 2010
4
Microsoft & Databricks certifications
Placement support: resume building, LinkedIn setup, mock interviews and interview preparation for every learner. We do not quote a placement percentage, because no institute can evidence one.
Learning modes and upcoming batches
Three months, classroom in KPHB or live online with the same trainer.
15 Oct
2026
• Monday to Friday
• Evening batch
• Same trainer, same syllabus
18 Oct
2026
• Saturday and Sunday
• Morning batch
• Built for working professionals
Current fee: confirmed per batch by a counsellor on +91 98824 98844 or WhatsApp. We would rather quote you the live figure than publish one that goes stale.
Check us out before you enrol
We would rather you verify everything than take our word for it.
Read the reviews
Our Google reviews are public and each links to the original. Read them before you call.
Look up the trainer
Bharat's LinkedIn profile is public. Check the experience claimed on this page against it.
Sit in on a class
Attend a live session free, ask the trainer whatever you want, and decide afterwards.
Azure AI engineer salary in Hyderabad
Indicative market ranges by role. These are estimates, not offers.
₹5–9 LPA
Indicative annual range, 0–2 years.
₹9–18 LPA
Indicative annual range, 2–5 years.
₹18–30 LPA
Indicative annual range, 5–8 years.
₹28–45 LPA
Indicative annual range, 8+ years.
Disclaimer. Figures are indicative market estimates and are not a guarantee of earnings. Actual pay depends on experience, skills, certifications, employer and interview performance. More detail in our Azure salaries in Hyderabad guide.
Career opportunities after Azure AI training
The roles this syllabus maps to.
Azure AI engineer
Builds and deploys AI apps and agents on Foundry. The role AI-103 is written for.
Generative AI developer
RAG pipelines, prompt design and model integration inside product teams.
AI solution architect
Chooses services and models, owns cost, security and responsible AI governance.
ML / AI platform engineer
CI/CD, quotas, scaling and observability for AI workloads.
Conversational AI developer
Agents, speech and multi-turn experiences.
Applied AI consultant
Advises on what AI can and cannot do for a given business problem.
More detail in our Azure job roles in Hyderabad guide.
From enrolment to interview-ready
14
Modules
5
Projects
AI-103
Certification track
Your career journey, mentored at every step.
From your first live session to your first interview, you have a named trainer accountable for the curriculum and a structure that does not leave gaps.
Enrol and onboard
Meet your trainer in the first live session. Roadmap and expectations set out clearly.
Get Python solid
Module 2, from scratch. No AI service is introduced until you can debug your own code.
Deploy your first model
A Foundry project, a deployed model and a working call from your own application.
Ground it
Retrieval, search and RAG, so the model answers from your data rather than inventing.
Build agents
Tools, memory, orchestration and approval flows.
Make it safe and observable
Filters, prompt shields, evaluation, tracing and audit.
Certify and apply
AI-103 preparation, resume and LinkedIn rebuilt, mock interviews.
Azure AI roadmap: from Python to certified
Six stages, and what you are actually accountable for in each.
Foundations
Python and the Azure basics everything else sits on.
The platform
Microsoft Foundry: projects, models, deployment and endpoints.
Generation
Prompting, parameters and structured output.
Grounding
Retrieval and search so answers come from your data.
Agents
Tools, memory, orchestration and safeguards.
Production
Safety, evaluation, observability, cost and the exam.
How this institute differs from other Azure AI training institutes in Hyderabad
Category-level comparison. We do not name competitors.
Feature
This institute
Commonly seen elsewhere
Exam taught
AI-103, the current exam
AI-102, retired 30 June 2026
Agents
A third of the course
Often a single bonus session
Grounding
RAG built end to end, then evaluated
A demo notebook
Responsible AI
Its own module, examined
A slide near the end
Python
Taught from scratch if needed
Assumed
Cost and quota
Taught
Usually skipped
Placement claim
Support described, no percentage quoted
100% placement claimed
Fees
Quoted per batch by a counsellor
Advertised, then revised
Why Azure AI is a strong career move in Hyderabad in 2026
Three things specific to this city and this year.
The credential just reset
AI-102 retired in June 2026. Everyone is starting from the same line on AI-103, which is unusually good timing to certify.
Agents are where the hiring is
Hyderabad's capability centres are staffing agent and RAG teams now. The exam weighting follows the market for once.
It is application work
This is a developer role, not a research role. If you can build and debug software, the route in is short.
Where do I start? Python, then the platform, then generation, then grounding, then agents, then safety and the exam. In that order.
Frequently asked questions
The twelve questions counsellors are asked most about this course.
Is AI-102 still valid? Should I study for it?
No. AI-102 and the Azure AI Engineer Associate certification retired on 30 June 2026 — the exam can no longer be scheduled and the credential is no longer issued. If a syllabus you are offered still centres on AI-102, it is out of date. The current exam is AI-103, Developing AI Apps and Agents on Azure.
What is AI-103?
AI-103 is the exam that replaced AI-102. It covers planning and managing Azure AI solutions, generative AI and agents, computer vision, text analysis and information extraction, built on Microsoft Foundry. Its skills were published as measured from 16 April 2026.
I already hold AI-102. Is it worthless now?
A credential you earned before retirement stays on your transcript, but it can no longer be renewed and it is no longer issued to anyone new. For a current credential on your profile, AI-103 is the route.
Do I need Python?
Yes, AI-103 assumes it. If you do not have it, Module 2 teaches it from scratch before any AI service is introduced. Tell the counsellor your background so we place you correctly.
Is this the same as the prompt engineering course?
No. Prompt engineering is one topic inside Module 4 here. If you want prompting for its own sake rather than building and deploying applications, the prompt engineering course is the better fit.
What does the AI-103 exam cost?
Microsoft sets and changes the price, and it varies by country. Check the exam page on Microsoft Learn for the current figure rather than trust a number from any institute, including this one. The fee is paid to Microsoft, not to us.
Do I need an Azure subscription?
You will work in a Foundry project during sessions. We show you how to set up your own subscription and manage spend, including the free tiers and quotas, so you can practise between classes.
How much of the course is agents?
AI-103 weights generative AI and agentic solutions at 30–35%, the largest section, and the course follows that. Modules 7 and 8 are agents end to end, and agents reappear in three of the five projects.
Is there placement support?
Resume building, LinkedIn setup, mock interviews and interview preparation. We do not quote a placement percentage, because no institute can evidence one.
How long is the course?
Three months. Longer than our Fabric course because Python and the breadth of AI-103 need the time.
Classroom or live online?
Same trainer and syllabus. Both are live, neither is recorded playback. Recordings are a catch-up option for the course duration.
Will this be out of date in a year?
Parts of it will. Model names and portal layouts move fast. The durable parts — retrieval, grounding, evaluation, agent design and responsible AI — are what we spend the time on, and we say plainly which parts are volatile.
Ready to start building on Azure AI?
Sit in on a live session before you commit. No obligation.
14
Curriculum modules
5
Real-time projects
AI-103
Current credential taught
Related reading
AI-powered data engineering on Azure
Where AI meets the data platform, and which role does what.
Visit or contact us
Our centre is in KPHB, Kukatpally, a short walk from JNTU College Metro Station.
Phone
Address
3rd Floor, Dr Atmaram Estates, near Metro Station JNTU College, beside Sri Bhramaramba Theatre, Jai Bharat Nagar, Hyder Nagar, Vasantha Nagar, Hyderabad, Telangana 500072
Hours
Monday to Saturday, 9:00 am – 8:00 pm