100+ Hours | Online & Live | Weekend Program

Artificial Intelligence & Data Science with GenAI Applications in Rail

A 6-month programme from IITM Pravartak, Zenith Railway Academy and industry partners. You get academic lectures from IIT Madras faculty and rail-domain experts, hands-on practical sessions and assignments, and a capstone project — all focused on applying AI to rail communication, operations and safety.

Live Sessions

100+

Hours Online & Live

Capstone

Applied & Assessed

IITM

Pravartak Certificate

Apply NowSpeak with an Advisor

Offered by IITM Pravartak Technologies Foundation in collaboration with Zenith Railway Academy and industry proponents

AI & Data Science Lab

Rail AI & GenAI Applications

Duration6 Months

Learning100+ Hours

FormatOnline / Weekend Classes

CertificateIITM Pravartak

Batch02

Live Sessions25 sessions across 6 months

Next Cohort6 June 2026

Fee₹80,000 + GST

Python & Power BI/Predictive Analytics/Deep Learning/Generative AI/AI Ethics & Governance/Capstone Project/

IITM Pravartak campus

About the innovation hub

IITM Pravartak — Innovation Hub of IIT Madras

IITM Pravartak Technologies Foundation is the Technology Innovation Hub (TIH) hosted by IIT Madras and funded by the Department of Science & Technology, Government of India, under the National Mission on Interdisciplinary Cyber-Physical Systems (NM-ICPS). It advances research and technology development in Sensors, Networking, Actuators and Control Systems (SNACS) within cyber-physical domains. Its work spans academia, industry, government and international partners — with a focus on entrepreneurship, skill-building and translational research.

Hosted by

IIT Madras

Funded under

NM-ICPS · DST, Govt. of India

Focus

Sensors, Networking, Actuators & Control Systems (SNACS)

Alumni Trained

15,000+ professionals worldwide

This programme is delivered within IITM Pravartak's technology and skilling ecosystem — an independent, verifiable reference point.

Why rail AI is different

A Safety & Operations Challenge, Not Just a Data Science Course

Rail AI spans edge devices (drones, POS terminals, digital signalling equipment), customer-facing systems, coach and queuing optimisation, predictive maintenance of hardware and software assets, and safety-critical detection tasks like track-crack and signalling-defect identification.

The key distinction: in rail, AI models must be trustworthy enough to inform safety-relevant decisions, not just optimise convenience.

Edge & Operational Data

Drones, POS terminals and digital signalling equipment generate the data AI models learn from.

Predictive Maintenance

Track quality, defect detection and electric/electronic predictive systems.

Safety-Critical Deep Learning

Crack detection, defective-signalling identification and pilot-attention monitoring.

Generative AI for Rail Problem-Solving

GenAI applied to rail-specific case studies, not generic chatbot use.

Ethics, Governance & Regulation

Bias, privacy, transparency and the regulatory frameworks governing AI in railways.

Capabilities you will build

What You Will Be Able To Do

Applied, rail-relevant capability across data analysis, predictive modelling, deep learning and generative AI.

Apply AI and data-science fundamentals to rail-domain problems.

Analyse data from edge devices, digital signalling equipment and customer feedback.

Perform descriptive analysis in Python (Matplotlib) and build interactive dashboards in Power BI.

Apply descriptive and statistical analytics to coach and queuing optimisation and resource allocation.

Build a simple automated customer-chat system for rail customer queries.

Apply predictive analytics to railway hardware/software and electric/electronic maintenance systems.

Apply deep learning to rail safety use cases — track-crack detection and defective-signalling identification.

Use generative AI techniques for rail-domain applications, including a hands-on generative-model exercise.

Integrate AI technologies into railway operations, including implementation-challenge planning.

Apply ethical guidelines and interpret the regulatory framework for AI in railways.

Build a business case and cost-benefit analysis for an AI project in a rail organisation.

Deliver a capstone project applying AI/data science to a real rail challenge.

Who should attend

Engineers and Professionals Applying AI to Rail

Pathway 1

Rail & Transport Engineers

Engineers seeking to apply AI knowledge in rail projects
Professionals at organisations providing railway products & services
Professionals who design or implement railway or transport solutions

Pathway 2

Technology Graduates & Working Professionals

Working professionals seeking a weekend, online upskilling path
Technology graduates entering AI-driven mobility roles
Professionals with rail industry experience (given preference in admissions)

It is not positioned as

A general-purpose computer-science or AI degree
A GenAI-only or prompt-engineering-only course
A course requiring advanced prior AI skills to enrol

You do not need advanced prior AI skills to apply — basic familiarity with computers and analytical reasoning is enough to get started.

Is this programme for you

A 60-Second Fit Check

Select the statements that describe you. This is a self-assessment guide — not a formal eligibility decision.

Your fit

0 / 6 selected0%

Select statements above

Tick the statements that apply to you to see how well this programme fits your goals.

Speak with an advisor

Who you will learn with

Engineers and Technologists, Building Rail AI Together

The peer-learning value lies in bringing rail engineers and technology professionals into the same room to apply AI to real rail problems.

8 yrs

Avg. experience

25+

Organisations

50 : 50

Rail : Tech split

30%

Prior AI/ML exposure

Professional background

Signalling & Rail Operations55%

IT / Data / Software60%

Rolling Stock & Maintenance35%

Project & Programme Management40%

Experience distribution

0–3 yrs

4–7 yrs

8–12 yrs

13+ yrs

Application interests

80%

Predictive Maintenance

88%

Generative AI

70%

Safety Analytics

65%

Data Visualisation

The learning journey

From AI Foundations to a Rail Capstone Project

01Stage

Establish AI & Data Foundations

Introduction to AI and data science, rail data sources, and descriptive analytics with Python and Power BI.

02Stage

Apply Analytics to Rail Operations

Statistical analytics for coach and queuing optimisation, resource allocation, and automated customer chat.

03Stage

Move into Deep Learning & GenAI

Deep learning fundamentals, generative AI for rail applications, and safety-focused detection use cases.

04Stage

Integrate, Govern & Plan

AI integration in operations, ethics, regulatory frameworks, project management and business-case development.

05Stage

Look Ahead

Future trends, industry perspectives and career pathways in AI for rail.

06Stage

Complete the Capstone

Present a capstone project applying AI and data science to a real rail challenge, evaluated by peers and instructors.

Curriculum architecture

25 Sessions, Grouped Into Six Pillars

Grouped from the programme's published session-by-session outline; group titles and structure are an editorial grouping of the real 25 sessions, not verbatim from raw.

01Foundations: AI & Data in the Rail Domain (Sessions 1–2)

Introduction to AI and its applications in the rail domain

Importance of data in railways and future implications

Digitization requirements for railways

Current data ingestion in railways: edge devices (drones, POS terminals, digital signalling equipment)

Customer feedback data and its significance

02Descriptive & Statistical Analytics (Sessions 3–5)

Descriptive analysis with Python (Matplotlib)

Advanced data visualisation with Power BI

Interactive dashboards for railway data

Descriptive and statistical analytics: coach & queuing optimisation, resource allocation

03Applied AI in Rail Operations (Session 6)

Automated customer chat systems

Building a simple chatbot for railway customer queries

04Predictive Maintenance & Deep Learning (Sessions 7–13)

Predictive analytics in the railway hardware/software ecosystem

Track quality monitoring and defect detection

Electric and electronic data-based predictive systems

Introduction to deep learning in the rail domain

Generative AI for rail domain applications (specialisation)

Deep learning applications in rail safety: crack detection, defective-signalling identification

Deep learning for enhanced pilot attention (specialisation)

Advanced deep learning techniques for railways (specialisation)

05Integration, Ethics & Governance (Sessions 14–20)

Integration of AI technologies in rail operations

Ethical considerations in AI applications for railways: bias, privacy, transparency

Regulatory framework for AI in railways

Real-world implementation challenges

Project management in AI implementation for railways

Cost-benefit analysis of AI implementation in railways

Business case development for AI projects in railways

06Future Trends & Capstone (Sessions 21–25)

Future trends in AI for the rail domain

Industry perspectives on AI adoption in railways

Career opportunities in AI for rail professionals

Capstone project presentation

Course conclusion and next steps

Tools & techniques in context

Tooling Taught — And What You Do With It

Only tools and techniques actually named in the published curriculum are listed here.

Python (Matplotlib)

Application

Descriptive data analysis and visualisation for railway data.

Power BI

Application

Advanced data visualisation and interactive dashboards for railway data.

Machine Learning (Supervised & Unsupervised)

Application

Core modelling techniques underpinning predictive analytics in rail.

Deep Learning

Application

Applied to predictive maintenance and rail safety detection tasks.

Generative AI (GenAI)

Application

Rail domain-specific generative-model exercise, taught as a specialisation session.

AI Ethics & Governance

Awareness

Bias, privacy, transparency and the regulatory frameworks governing AI in railways.

Hands-on practice

Practise Building Rail AI Systems, Not Just Studying Them

Hands-on exercises accompany the curriculum, including a generative-model build and a customer-service chatbot. Access details and prerequisites are confirmed in the brochure.

IITM Pravartak learning environment

Toolset

Python & Matplotlib

Power BI

Machine learning & deep learning frameworks

Generative AI tooling

Applied cases

Build interactive Power BI dashboards for railway data

Build a simple chatbot for railway customer queries

Detect track cracks and defective signalling with deep learning

Build a generative model for rail-domain data (hands-on exercise)

Complete a capstone project applying AI to a real rail challenge

For every case learners receive

Live faculty-led sessions

Recorded sessions for flexible learning

Assignments and lab exercises

Mentorship and guidance

Career and placement guidance

ZRA Labs Live sandbox

Labs in all 8 domains

ZRA Labs · Hands-on practice

Executive courses come with a hands-on lab.

ZRA Labs is the hands-on practice app included with every Railway Academy executive courses — run guided exercises and live simulations across all eight domains in your browser, and build skills you can use on the job.

Interactive domain simulations
Guided, auto-graded exercises
Browser-based — zero setup
Included with all Executive Courses

Explore ZRA Labs

Programme faculty & industry experts

IITM Pravartak Faculty & Industry Practitioners

Raw programme materials name IITM Pravartak faculty and industry practitioners generically, without an individual faculty roster for this specific programme — named individuals below are unverified against this raw source and are carried over from the sibling IITM Pravartak cybersecurity programme's faculty pool where domain-appropriate.

AI & Cybersecurity

IITM Pravartak

Ashish Arora

Teaches AI Applications

Ashish Arora

Teaches AI Applications

Ashish Arora is a technology and artificial intelligence professional focused on AI-driven product development, machine learning, and digital innovation. He has been associated with AI and software engineering initiatives involving generative AI, intelligent automation, and enterprise technology solutions. His work reflects strong expertise in AI applications, product strategy, and emerging technologies, with active involvement in the broader AI and tech ecosystem.

Programme & Academic Leadership

IITM Pravartak

Dr. L. Subramanian

Cyber-Physical Systems

Dr. L. Subramanian

Cyber-Physical Systems

Foundations of secure cyber-physical infrastructure.

AI & Cybersecurity

IITM Pravartak

Madhusudhanan (Madhu) Baskaran

Teaches AI

Madhusudhanan (Madhu) Baskaran

Teaches AI

Chief Data & AI Strategist and Ph.D. in AI/ML with 32+ years of cross-functional experience spanning AI, Data, Engineering, R&D, and product innovation. Specializing in Generative AI, NLP, LLMs, Speech AI, Document Intelligence, Responsible AI, and Intelligent Systems. Guest Faculty at IIT Madras and Thought Leader – AI at IITM Pravartak, with extensive experience advising enterprises, governments, startups, and technology leaders.

Capstone & portfolio

Apply AI and Data Science to a Real Rail Challenge

Participants present a capstone project applying AI/data science to a real rail problem, with feedback and evaluation from peers and instructors and recognition for outstanding projects.

Formats & deliverables

Problem statement grounded in a real rail challengeData analysis and model-building approachApplied AI/ML or GenAI solutionEvaluation of results and limitationsFinal presentation

Evaluation & structure

01Technical accuracy

02Rail-operational relevance

03Data and modelling rigor

04Communication and presentation

05Practical feasibility

Anonymised examples from previous cohorts

Predictive maintenance model

Apply predictive analytics to railway hardware/software or electric/electronic asset data.

Safety detection use case

Apply deep learning to detect track cracks or defective signalling from operational data.

GenAI rail application

Build a generative-AI solution addressing a rail domain problem, extending the hands-on GenAI session.

Credential

Earn a Professional Certificate from IITM Pravartak

On successful completion, participants receive a Professional Certificate awarded by IITM Pravartak Technologies Foundation, in partnership with Zenith Railway Academy.

Professional Certificate Program AI & Data Science with GenAI: Applications in Rail certificate

How you are assessed & completion criteria

SignatoriesDr. M J Shankar Raman (CEO, IITM Pravartak Technologies Foundation) & Mr. Sumit Kanu (Director, Zenith Railway Academy)

Awarding bodyIITM Pravartak Technologies Foundation

CapstoneCapstone project presentation and evaluationRequired

Certificate formatDigital + physical

Career & organisational application

Apply the Learning Across Rail AI Roles

Technical Roles

AI/data science analyst in a rail organisation

Predictive-maintenance analyst

Rail data & analytics engineer

Applied & GenAI Roles

Generative-AI applications specialist

AI-driven customer experience lead

Leadership & Governance Roles

AI project/programme manager

AI policy or governance advisor for rail operations

Organisational applications

Predictive maintenance programmes

Coach and queuing optimisation

Customer-service automation

Rail safety analytics

AI governance and ethics policy

Digital transformation planning

The programme does not guarantee placements. It provides career guidance, resume/LinkedIn support and exposure to industry hiring trends; outcomes depend on prior experience, technical depth and market conditions.

Schedule & workload

Designed Around Working Professionals

Duration

6 months

Total learning

100+ hours

Live sessions

25 sessions, weekends online

Self-paced work

Exercises and project work during the week

Capstone

Final project presentation

Eligibility & selection

Eligibility, Prerequisites & Readiness

This is a Post Graduate Certificate programme, designed for engineers/professionals who wish to learn and apply AI knowledge in their rail-project roles, worldwide.

Engineers/professionals at organisations providing railway products & services, or that design/implement railway or transport solutions, may also apply.

Indian participants: graduates (10+2+3) or diploma holders (10+2) from a recognised university, in any discipline.

International participants: a graduation or equivalent degree from any recognised university or institution in their country.

Preference is given to professionals with rail industry experience.

You do not need advanced prior AI skills to apply — basic familiarity with computers and analytical reasoning is enough.

Fees & payment

Fees & Payment Options

Total programme fee is ₹80,000 + GST, payable in three instalments, with a refundable ₹3,000 registration fee at application and a no-cost 10-month EMI option through the loan partner.

Flexible

Instalments

Three instalments — ₹30,000 + GST immediately after the offer letter, ₹25,000 + GST within 30 days of class commencement, ₹25,000 + GST within 90 days of class commencement (US $600 per instalment for international students).

Enquire

No-cost EMI