Keerthan HS
Data-focused AI Intern with hands-on experience in building end-to-end computer vision pipelines, including data collection, annotation, preprocessing, model training, and deployment. Experienced in working with image datasets, defect detection, and deep learning models using Python and PyTorch.
Strong understanding of dataset quality, augmentation, and real-world deployment for scalable AI systems.
Experience
AI / Computer Vision Intern (Software Intern)
L2MRail
Collected and curated large-scale image datasets for defect detection and object classification.
Annotated images using bounding boxes and labeling tools.
Performed data cleaning and preprocessing to ensure dataset quality.
Built and trained CNN models using PyTorch.
Implemented data augmentation techniques.
Evaluated model performance and improved accuracy.
Tested models on real-world data.
Deployed trained models into a web-based system.
Collaborated with team members to improve dataset and model performance.
Education
Sai Vidya Institute of Technology
Bachelor of Engineering
CGPA: 8.72
Skills
Projects
Production-Style ETL Pipeline using PySpark & Databricks (Github)
Designed and implemented an end-to-end ETL pipeline using PySpark and Databricks to ingest, clean, transform, and store raw CSV data in optimized Parquet format.Performed data annotation and labeling to generate bounding box annotations for supervised learning.
Implemented Medallion Architecture (Bronze, Silver, Gold), schema validation, incremental data loading, window functions, logging, and error handling to simulate a production-grade data engineering workflow.
Developed analytics-ready datasets through data aggregation and transformation, improving query performance using partitioned Parquet storage.
Pothole Detection System using Computer Vision & Deep Learning
Collected and curated pothole image dataset from Kaggle for model training
Performed data annotation and labeling to generate bounding box annotations for supervised learning
Preprocessed dataset and split into training and validation sets (80:20 ratio)
Designed and configured custom YAML file to define dataset paths and class labels
Trained an object detection model (e.g., YOLO) on annotated dataset
Evaluated model performance using validation data and unseen test images
Implemented inference pipeline to detect potholes and generate bounding box predictions on new images
Visualized detection results for performance analysis and validation
Delivered actionable insights and detection outputs to relevant government authorities for infrastructure monitoring
End-to-End Industrial Defect Detection Pipeline (Internship Project)
Managed the complete machine learning pipeline from data acquisition to model deployment in a production-like environment
Collected and organized datasets from secure cloud storage (e.g., OneDrive) for multiple industrial components
Performed image annotation and labeling for object detection and anomaly detection tasks
Trained and optimized YOLO-based object detection models for identifying multiple component classes (e.g., rods, springs, mechanical parts)
Implemented and evaluated deep learning models including ResNet50, ResNet101, and PaDiM for classification and anomaly detection
Conducted model validation and testing on unseen data to ensure robustness and accuracy
Built inference pipelines to generate predictions and integrate outputs into downstream systems
Deployed trained models into an internal GUI-based application for real-time or batch predictions
Collaborated in improving data quality, annotation consistency, and model performance across iterations
Ensured adherence to data confidentiality and secure handling practices throughout the project
Certification
Complete Data Analyst Bootcamp
UDEMY
Data Structures and Algorithms
UDEMY
Achievements
2nd prize in mini project
Siren Based Ambulabce Detection System
Secured 2nd prize in our mini project conducted in 6th semester