AI & Machine Learning Labs | Quadratech Educational Solutions

Computing Infrastructure

GPU-Powered Workstations

High-performance workstations with NVIDIA GPUs, configured for deep learning training and inference at scale.

ML Frameworks

Pre-Configured AI Stack

TensorFlow, PyTorch, Scikit-learn, Keras, and Hugging Face — all pre-installed and ready for training sessions on day one.

Applied AI

Computer Vision Projects

Object detection, face recognition, pose estimation, and real-time video analytics using cameras and edge devices.

Research Outcomes

Publication-Ready Research

Students build research-grade AI projects suitable for national competitions, publications, and industry internships.

01 / 04

40+Labs Installed

3,000+Students Trained

20+AI Projects / Batch

2yrWarranty & AMC

Lab Equipment

Every AI/ML lab comes fully equipped with hardware and software needed for end-to-end machine learning workflows.

GPU Workstations

NVIDIA RTX series GPUs with 16GB+ VRAM, 64GB RAM, and NVMe SSDs for fast model training.

Edge AI Devices

NVIDIA Jetson Nano, Jetson Xavier, and Raspberry Pi 4 for edge inference and deployment demos.

Vision Hardware

High-res USB cameras, Intel RealSense depth cameras, and IR sensors for computer vision projects.

Dataset Library

Curated offline datasets covering image classification, NLP, time-series, and tabular data problems.

Cloud Lab Access

Managed access to AWS SageMaker and Google Colab Pro for large-scale distributed training.

Network Storage

Shared NAS storage with 10GbE switch for dataset sharing and collaborative model development.

Curriculum Structure

A progression from Python basics to advanced deep learning, with real project deliverables at every stage.

Module 1 — Python & Data

Python programming, NumPy, Pandas, and data visualisation for ML workflows.

Module 2 — Classical ML

Supervised, unsupervised, and reinforcement learning algorithms with Scikit-learn.

Module 3 — Deep Learning

CNNs, RNNs, Transformers, and generative models with TensorFlow and PyTorch.

Module 4 — MLOps & Deployment

Model deployment, API integration, Docker containerisation, and monitoring pipelines.

Trusted by Leading Institutions Across Kerala

CUSATCochin University of Science & Technology

NIT-CNIT Calicut

CETCollege of Engineering Trivandrum

TKMTKM College of Engineering

MECModel Engineering College

GEC-TGovt. Engineering College Thrissur

SNGCESNG College of Engineering

FISATFederal Institute of Science & Tech

LBSLBS Institute of Technology for Women

VIDYAVidya Academy of Science & Tech