Now Scheduling Engineering Colleges Across India
AI · ML · MLOps Workshops

Build the AI Skills
Engineers
Need to Lead.

Domain-specific. Portfolio-ready. Industry-aligned AI/ML workshops for engineering students and faculty — ECE, EE, AIML & CS. Hands-on from Day 1. Certified on Day 3.

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16+
Years Experience
3
Active Programs
18+
Hours / Workshop
100%
Hands-On Labs
Active Programs
AI/ML for ECE Students
● Live
3 Days  ·  18+ Hours ₹1,200/student Min. 120
AI for Electrical Engineers
● Live
3 Days  ·  EE Domain ₹1,200/student Min. 120
MLOps: Notebook → Production
Popular
3 Days  ·  Cloud Deploy ₹1,200/student Min. 60
Faculty Development Program
FDP
Custom Duration Contact for Pricing
Our Programs

Industry-aligned workshops built for every engineer

Every workshop is domain-specific — content, datasets, code labs, and capstone projects are 100% tailored to the student's engineering branch. Not generic. Not repackaged.

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📡
LiveSDP

AI/ML Workshop for ECE Students

From AI fundamentals to LLMs and GitHub Copilot — every example contextualised for Electronics & Communication Engineering.

D1AI Fundamentals & Practical Applications — Supervised/Unsupervised Learning, Python ML Libraries
D2LLMs & Prompt Engineering — ChatGPT, Gemini, Copilot, Zero/Few-Shot, Chain-of-Thought
D3Advanced Applications & Careers — GitHub Copilot, Responsible AI, Live Project Presentation
PythonMLLLMsPrompt Eng.GitHub Copilot
₹1,200 +GST
Min. 120 students
Get Quote →
⚡
LiveEE Domain

AI for Electrical Engineers

Every use case mapped to EE — power system fault detection, solar forecasting, energy theft detection, and a GATE study bot.

D1AI Foundations for EE — Solar power forecasting pipeline, 3-phase fault classification with Random Forest
D2Deep Learning + Modern AI — LSTM load forecasting, Transformer with EE intuition (embeddings as phasors)
D3Build Day — GitHub Copilot, Solar Dashboard, Smart Energy Anomaly Detector, GATE RAG bot
Random ForestLSTMGradioLangChainRAG
₹1,200 +GST
Min. 120 students
Get Quote →
🚀
LiveAIML Dept

MLOps: From Notebook to Production

Takes students from notebook-level ML to a fully deployed, live cloud ML system — CI/CD, monitoring, and a shareable GitHub portfolio link.

D1MLOps Foundations & Experiment Tracking — MLflow, DVC, Evaluation Gates, Model Registry
D2Model Serving & Cloud Deployment — FastAPI, Docker, AWS EC2, GitHub Actions CI/CD, Live URL
D3Monitoring, Interview Prep & Capstone — Evidently drift monitoring, Loan Default Prediction pipeline
MLflowFastAPIDockerAWS EC2Evidently
₹1,200 +GST
Min. 60 students
Get Quote →
🎓
FDPFaculty

Faculty Development Program

Upskill your faculty in AI/ML fundamentals, GenAI tools, and industry-oriented teaching methodologies — custom-designed for your department.

◆AI/ML Curriculum Design for Engineering Departments
◆Hands-on with GenAI Tools: NotebookLM, Gamma, Copilot for Teaching
◆Responsible AI, Bias Awareness & Industry Integration
For HODsFor ProfessorsCustom Duration
Custom
Contact us for pricing
Contact Us →
◆ Why Krysha Academy

Not just another
coding workshop.

"We don't run generic workshops. Every program is engineered from the ground up for your students' engineering discipline."

Krysha Academy vs Generic Training
FeatureGenericKrysha
Domain-specific content✕✓
GitHub portfolio projects✕✓
Live cloud deployment✕✓
Faculty performance portal✕✓
Post-training WhatsApp support✕✓
Industry mentor (16+ yrs)✕✓
🎯

Domain-First Design

Every case study, dataset, and project is drawn from the student's engineering discipline — not generic examples.

💼

Portfolio-Ready Output

Students graduate with 3+ GitHub-ready projects they can share with recruiters from Day 1.

☁️

Real Cloud Deployment

MLOps students get a live, public URL for their ML API deployed on AWS EC2 — a genuine production artifact.

📊

Faculty Admin Portal

TPOs and HODs get real-time access to attendance, performance, and engagement dashboards.

🔒

Policy-Compliant

Clear terms on session confirmation, cancellation charges, and laptop prerequisites — no surprises.

🤝

Post-Training Support

Dedicated WhatsApp group for resolving doubts during and after the workshop, plus dashboard access.

Expert Mentors

Taught by industry veterans,
not just academics

Our mentors bring real-world, production-grade experience to every session — bridging the gap between academic theory and industry practice.

Mentor 01
B

Industry Expert

Lead Trainer · Krysha Academy
⭐ 16+ Years Industry Experience

AI/ML educator and industry veteran transforming complex technology into career-advancing skills. Our lead trainer has led corporate training programs and college workshops across India, bringing full-stack engineering and AI/ML depth to every session. Expertise spans the intersection of AI/ML with domain engineering — from power systems to IoT to enterprise software.

Java · Spring BootPython · ML · FastAPIReact · AngularAWS · AzureDocker · MLflow · DVCAI/ML Curriculum Design
Mentor 02
V

Industry Expert

Senior Trainer · Full-Stack AI Engineer
⭐ 11+ Years IT Industry Experience

A full-stack AI engineer who has built complex AI/ML applications from frontend to cloud infrastructure. Our senior trainer specialises in end-to-end ML deployment — from FastAPI model APIs to Docker containerisation to GitHub Actions CI/CD. Delivering hands-on, developer-first workshops that connect the complete AI engineering stack.

Java · React · AngularAWS · Cloud DeployFastAPI · DockerGitHub ActionsAgentic AICorporate Training
Curriculum Deep Dive

What your students build, not just learn

Each workshop progresses from concept to code to production. Select a program to explore the day-by-day curriculum.

AI/ML Workshop for ECE Students

A ground-up AI/ML workshop for Electronics & Communication students. Students finish with practical ML, LLMs, prompt engineering, and GitHub Copilot skills — and present a live project on Day 3.

⏱️3 Days · 18+ Hours · 9:30 AM – 5:00 PM
👥120 Students Minimum · Multiple Batches Available
💻Personal laptops required from Day 1
📜Krysha Academy Certificate on Completion
Request This Workshop →
D1
AI Fundamentals & Practical Applications
9:30 AM – 5:00 PM
+
01Introduction to AI concepts and the AI/ML/DL/GenAI hierarchy with real-world examples
02Machine Learning types — Supervised, Unsupervised, Reinforcement Learning
03Python & ML libraries setup: NumPy, Pandas, scikit-learn hands-on labs
04Real-world ML case study with Indian tech industry context
05Team formation and day-end quiz competition
D2
LLMs & Prompt Engineering
9:30 AM – 5:00 PM
+
01Large Language Models — how GPT, Gemini, Claude, and Copilot work under the hood
02Prompt Engineering principles and the CSIC Framework
03Zero-shot, Few-shot, Chain-of-Thought, and Role-Play prompting hands-on
04Hands-on: ChatGPT, Gemini, GitHub Copilot, NotebookLM, Gamma.app
05Team presentations — "Prompt Ninja of the Day" challenge
D3
Advanced Applications & Future Opportunities
9:30 AM – 5:00 PM
+
01GitHub Copilot — live demo + hands-on AI pair-programming session
02Responsible AI & understanding bias in AI/ML systems
03AI career roadmap — paths in ML, MLOps, AI Product, Research
04Final project development, student presentations, and certificate ceremony

AI for Electrical Engineering Students

Every concept is mapped exclusively to EE domain problems — power system fault detection, solar generation forecasting, energy theft detection, and an intelligent GATE study bot.

⏱️3 Days · 18+ Hours · 9:00 AM – 5:00 PM
📦3 Main Portfolio Projects + 1 Bonus RAG App
🔬100% EE domain-specific datasets and code labs
🐙All projects are GitHub-ready from Day 3
Request This Workshop →
D1
AI Foundations for Electrical Engineers
9:00 AM – 5:00 PM
+
01AI/ML/DL/GenAI hierarchy with live EE context — Relay → SCADA → GenAI evolution
02Python & Colab: NumPy for AC signal processing; Pandas for smart-meter data
03Linear Regression — Solar power forecasting pipeline (irradiance → output)
04Random Forest — 3-phase electrical fault classification; Confusion Matrix, Precision/Recall
🏗️ Project 1 Introduced
Power System Fault Classifier: 3-phase fault detection using LR + DT + RF algorithm comparison with feature importance and confusion matrix
D2
Deep Learning + Modern AI Tools
9:00 AM – 5:00 PM
+
01ANN / CNN / LSTM — load forecasting and power-quality waveform classification
02LLM & Transformer architecture with EE intuition — embeddings explained as phasors
03Prompt Engineering: CSIC Framework — Zero/Few-Shot, Chain-of-Thought, Role-Play
04AI Tools: NotebookLM, Gamma.app; model comparison and hyperparameter tuning
🏗️ Project 2 Introduced
Solar Power Generation Forecasting: 30-min ahead forecast for DISCOM grid scheduling. Random Forest + Feature Engineering + Gradio prediction UI
D3
Build Day: Real EE-AI Systems + Ethics + Career
9:00 AM – 5:00 PM
+
01GitHub Copilot — AI pair-programming for 3-phase power functions
02Capstone A (Demo): Solar Power Forecasting Dashboard — Random Forest + Gradio UI
03Capstone B (Students Build): Smart Energy Anomaly Detector — Isolation Forest + Gradio
04Bonus: GATE EE Smart Study Bot — RAG with LangChain + Gemini + FAISS + Gradio
05Responsible AI for Critical Infrastructure, career roadmap, and certificate ceremony
🏗️ Projects 3 + Bonus
Smart Energy Anomaly Detector: Isolation Forest (unsupervised ML) + Gradio UI
GATE EE Smart Study Bot (Bonus): RAG chatbot — LangChain + Gemini + FAISS + Gradio

MLOps: From Notebook to Production

Designed for AIML undergraduates (2nd year+). Takes students from notebook ML to a fully deployed, live cloud ML system in 3 days. Every student leaves with a live public URL.

☁️Every student deploys to AWS EC2 — live public URL guaranteed
⏱️3 Days · 18+ Hours · 9:00 AM – 5:00 PM
💻Min. 8 GB RAM, Docker-compatible OS, admin access required
📦Complete 7-layer production ML pipeline · shareable GitHub link
Request This Workshop →
D1
MLOps Foundations & Experiment Tracking
9:00 AM – 5:00 PM
+
01What is MLOps? Live demo of real notebook failures in production — why it matters
02The 7-layer production ML pipeline — full picture of what the course builds
03Experiment Tracking with MLflow — log 5 experiments, compare in UI
04Data & Model Versioning with DVC — track datasets, configure remote storage
05Evaluation Gates + MLflow Model Registry — automated quality gates
D2
Model Serving & Cloud Deployment
9:00 AM – 5:00 PM
+
01Model API with FastAPI — build /predict and /health endpoints, Swagger UI testing
02Containerisation with Docker — write Dockerfile, build image, push to registry
03Cloud Deployment — AWS EC2 (primary) or Render.com; every student gets a live URL
04CI/CD with GitHub Actions — auto-test on push, auto-deploy Docker image on merge
D3
Monitoring, Interview Prep & Capstone
9:00 AM – 5:00 PM
+
01Model Monitoring with Evidently — drift reports, simulate concept drift
02Interview Prep — Top 10 MLOps interview questions, resume bullet formula
03Capstone — Loan Default Prediction: complete 7-layer pipeline independently
04Project presentations: live endpoint, MLflow UI, CI badge & monitoring report
🏗️ Capstone Portfolio Project
Loan Default Prediction: complete 7-layer production ML pipeline — MLflow + DVC + FastAPI + Docker + AWS + GitHub Actions + Evidently. Shareable GitHub + live public URL.
How It Works

From enquiry to execution
in 4 steps

1

Send Enquiry

Reach out via contact form, WhatsApp, or email with your department and student count

2

Get Quotation

Receive a detailed, customised proposal with curriculum, pricing, and terms within 24 hours

3

Confirm Dates

Mutually confirm training dates, student count, and laptop prerequisites. Setup guide sent one week in advance

4

Students Build

3-day intensive training with live projects. Students graduate with certificates and portfolio on Day 3

What Every Student Gets

Real proof of skills
from Day One

🏅

Krysha Academy Certificate

Official certification validating completion of an industry-aligned AI/ML workshop programme

🐙

GitHub Portfolio Projects

3+ production-grade, domain-specific projects pushed to GitHub and shareable with recruiters

☁️

Live Public URL (MLOps)

MLOps students deploy a live ML API to cloud infrastructure and receive a shareable public link

📚

Course Materials & Dashboard Access

Comprehensive materials plus post-training dashboard for resources and practice exercises

💬

Dedicated WhatsApp Support

Ongoing query resolution via dedicated support group during and after the workshop

📊

TPO / Faculty Admin Portal

Institutions get real-time visibility into attendance, performance, and individual student progress

Who Attends

Built for every kind of
engineering learner

📡
ECE Students
Electronics & Communication Engineering undergraduates learning AI for signal processing, communication systems, and IoT
2nd Year+Python Basics
⚡
EE Students
Electrical Engineering undergraduates applying AI to power systems, SCADA, smart grids, and energy analytics
2nd Year+EE Domain
🤖
AIML Students
AI/ML undergraduates ready to take their notebook skills to a full production ML deployment pipeline on the cloud
2nd Year+ML Basics
🎓
Faculty Members
Professors, HODs, and academic coordinators looking to integrate AI/ML into curriculum and modernise teaching methods
FDPCustom
◆ Coming Soon

More programs arriving across
all engineering streams

Register your interest to be first notified when new domain-specific workshops launch.

⚙️
AI for Mechanical Engineers
Predictive maintenance, computer vision for quality control, digital twins
🏗️
AI for Civil Engineers
Structural health monitoring, smart city analytics, material defect detection
🏭
Industry 4.0 Programs
IIoT, cyber-physical systems, industrial automation with AI
🧠
GenAI Programs
Generative AI tools, RAG systems, Agentic AI for enterprise applications
🤖
Agentic AI Programs
AI agents, multi-agent systems, tool-use, memory, and planning
📊
Applied Data Science
End-to-end data pipelines, feature engineering, advanced analytics
FAQ

Frequently asked questions

About our workshops, policies, and how to get started.

Still have questions?

We respond within a few hours on business days.

Email Us →
For ECE and EE workshops, the minimum batch size is 120 students. For the MLOps workshop, the minimum is 60 students (B.Tech/B.E. AIML, 2nd year and above). Multiple batches can be arranged as needed by the college.
Students must bring personal laptops. For the MLOps workshop, a minimum of 8 GB RAM and a Docker-compatible OS with admin access is required. A pre-workshop setup guide is shared 1 week before training begins.
Training sessions cannot be cancelled at short notice once dates are mutually confirmed. Trainers are pre-committed to subsequent assignments, so extensions are not accommodated. If a confirmed session is cancelled, ticket and travel cancellation charges are borne by the institution.
The college is responsible for arranging food and accommodation for the trainers during the training period. A suitable classroom with projection and power access is required. For the MLOps workshop, students must complete the pre-workshop environment setup before Day 1.
A dedicated WhatsApp support group is maintained during and after the workshop. Students receive dashboard access for post-training resources. TPO/Faculty admins get access to track individual student performance and attendance records.
The standard pricing for all current in-person workshops is ₹1,200 per student plus 18% GST (total ₹1,416 per student). For Faculty Development Programs (FDPs), pricing is customised. Contact us for a detailed quotation.
Absolutely. Every Krysha Academy workshop is designed to be domain-specific. We can customise use cases, datasets, and projects to align with your department's focus areas. Contact us to discuss your specific requirements.
◆   ◆   ◆

Ready to bring AI
to your engineering college?

Reach out to discuss your department's requirements. We'll prepare a customised quotation and curriculum within 24 hours.

Request a Quote WhatsApp Us
📍 Bengaluru, Karnataka – 560098
✉️ contact@kryshaacademy.com
📞 +91 95389 93897  ·  +91 91103 21290
🌐 www.kryshaacademy.com