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PHN Technology × IITM Pravartak

Edge AIML And IoT System
Professional
Certificate

🎓 IIT Pravartak Madras Certificate
🌐 First Virtual IoT Access
📅 Weekend Only
👥 100 Seats Only
📄 Download Brochure
60
Live Hours
16
Sessions
4
Months
Graduates Hired At
NVIDIA
Qualcomm
Google
Intel
Bosch
Tata Elxsi
Tesla
Siemens
NVIDIA
Qualcomm
Google
Intel
Bosch
Tata Elxsi
Tesla
Siemens
NVIDIA
Qualcomm
Google
Intel
Bosch
Tata Elxsi
Tesla
Siemens
NVIDIA
Qualcomm
Google
Intel
Bosch
Tata Elxsi
Tesla
Siemens
NVIDIA
Qualcomm
Google
Intel
Bosch
Tata Elxsi
Tesla
Siemens
NVIDIA
Qualcomm
Google
Intel
Bosch
Tata Elxsi
Tesla
Siemens
🚀 About the Program

Mastering the Future of Edge Intelligence

This Professional Certification in Edge AIML And IoT System is a flagship initiative by IIT Pravartak Madras and PHN Technology.

Participants will gain hands-on experience through high-fidelity virtual simulations, learning to optimize models for constrained environments.

IIT Madras
IIT Pravartak Technologies Foundation
A Section 8 Company of IIT Madras, focused on Sensor, Networking, Actuators and Control Systems.
👨‍🏫 Your Instructors

Built by Researchers.
Delivered by Practitioners.

Prof. Babji Srinivasan

Prof. Babji Srinivasan

Professor, IIT Madras

Expert in ML, control systems, and cyber-physical systems. Designed the full curriculum architecture. Combines deep academic rigor with real-world system deployments. Secured ₹250M+ in competitive research funding.

150+
Papers
₹250M
Funding
27
H-Index
2300+
Citations
Dr. Ramji Srinivasan

Dr. Ramji Srinivasan

Former AI Lead, Qualcomm (UK)

AI Strategist and Deep Tech Specialist bridging research and real-world deployment. Former AI Lead at Qualcomm (UK) with 23+ years leading scalable AI systems for wearables, audio, and embedded platforms. Holds global patents in Edge AI.

23+
Yrs Leadership
20+
Patents
Ex-UK
Qualcomm
Global
Experience
📚 Curriculum

16 Sessions. 5 Modules.
One Complete Stack.

  • 01ML Foundations for Edge
  • 02Sensor Data Engineering
  • 03Model Training for Edge
  • 04Edge Deployment & Optimisation
  • 05Integration & Systems Thinking
  • 🏆Capstone Presentation
MOD 01–02
Sessions 1–2 · 6 Hours

ML Foundations for Edge

  • Core ML concepts for embedded AI perspective
  • Understanding memory, latency & power constraints
  • Virtual MCU & Simulation environment introduction
  • Environment setup & virtual data capture
  • Edge-aware model selection framework
⚙️ Live Case Studies

Real Systems.
Not Hypothetical Examples.

Weather Prediction on Tiny ML Systems
Building a real-time weather classification system using temperature and humidity data from DHT11 sensors, deployed on an 8-bit microcontroller with just 2 KB RAM.
Virtual MCUDHT11 SensorDecision Tree2KB RAM
Non-voice Audio Classification on Edge Devices
Classifying environmental sounds (e.g., faucet vs. noise) using a 1-D CNN model running on microcontrollers with on-board PDM microphones.
1-D CNNPDM MicFeature ExtractionOn-device
🎯 Outcomes

Capstone Deliverable

⚡

Fully Functional Edge AI System

On-device machine learning.

📂

Documented GitHub Pipeline

Full data-to-deployment workflow.

📊

Performance Evaluation Report

Accuracy, latency, and memory metrics.

🎤

Structured Technical Presentation

Walkthrough of trade-offs.

🎓

IITM Pravartak Certificate

Official programme certificate.

"It is a system you designed, built, and deployed."

🚀 Target Audience

Who is this Course For?

💼
Professional Experience
Minimum 2+ Years of Experience

Ideal for Embedded Systems Engineers, IoT Developers, ML Engineers, and Firmware Developers.

💻
Programming Skills
Proficiency in Python and basic understanding of C/C++.
📊
Math Foundations
Basic knowledge of linear algebra, calculus, and statistics.
🔌
Edge Intelligence
Curiosity about simulated sensors and virtual actuators.
🎓
Academic Background
B.E/B.Tech in CS, IT, ECE, EEE or related fields.
📈 Market Opportunity

Edge AI is the Fastest Growing
Tech Specialization

0
Max Salary — ML Systems Eng.
₹15–30 LPA range
💰
0
Interview Score Multiplier
With Edge AI demo
🚀
0
New AI Jobs Globally
WEF 2025 projection
🌍
0
Edge AI Job Growth
Source: Industry Reports
📊
💼 Career Outcomes

From ₹6–8 LPA to
₹14–22 LPA in 12 Months

Edge AI Engineer₹12–22 LPA
Embedded ML Developer₹14–28 LPA
ML Systems Engineer₹15–30 LPA
IoT AI Specialist₹10–20 LPA
🎓
IITM Pravartak Certificate
Faculty certificate recognised by top companies
💼
Portfolio with Real Deployed Systems
GitHub-documented, interview-ready
🚀
3× Interview Advantage
Deployed Edge AI demo multiplies score
⭐ Programme Differentiators

8 Reasons This
Programme is Different

01
IIT Madras Faculty
Designed and delivered by active IIT Madras professors with live industrial expertise.
02
Sensor to Deployment
Master the complete stack: sensor physics → quantization → on-device inference.
03
Engineering Thinking
Learn through trade-offs, baselines, failure modes, and real design decisions.
04
Small Cohort of 100
Every question addressed. Every capstone reviewed directly by the IIT Madras team.
05
Portfolio Artifact
Graduate with a working edge AI system, documented pipeline & measurable metrics.
06
Weekend Only
Designed for working professionals — build deeply without career interruption.
07
Interview Ready
Capstone built and presented to meet top product company hiring expectations.
08
First Virtual IoT Access
The first-ever program by IIT Pravartak Madras providing full virtual IoT access — master TinyML from anywhere.
Total Program Fee
₹60,000
(*Incl. Taxes)
Pay In Installments, as low as
₹20,000/month

We have partnered with the following financing company to provide competitive finance options at as low as 0% interest rates with no hidden cost.

razorpay
Admission Closes on : 31st May 2026
📋 How to Apply

3 Steps to Secure Your Seat

1
Apply Online
Submit your application: name, background, current role, motivation (3–5 sentences). GitHub link optional.
2
Fee Payment & Enrollment
Pay ₹60,000 (or ₹20,000 first EMI) to confirm your seat. Instant confirmation. Secure payment gateway.
3
Confirmation & Onboarding
Receive confirmation within 24 hours. Access details and onboarding materials sent ahead of June 5.
🔒 Secure payment gateway · 24hr confirmation email

100 Seats. One Cohort.
June 5, 2026.

Enrollment closes May 31, 2026. Once full, there is no waitlist for Cohort 1.

📄 Download Brochure🎙️ Join Free Webinar