Services
I offer
I collaborate with startups, labs, and teams to design, architect, and deploy intelligent solutions, from trained ML models to edge hardware prototypes that actually work in the field.
Computer Vision & Visual Forensics
Custom deep learning pipelines for image classification, target detection, and digital forensics.
- Fine-grained image classification models (ResNet, MobileNet, custom CNNs)
- Explainable AI integration using Grad-CAM attention heatmaps
- Digital image forensics (Error Level Analysis ELA, high-frequency DCT GAN analysis)
- Adversarial protection techniques (FGSM noise mapping)
Machine Learning & Natural Language Processing
Advanced classification, recommendation systems, and semantic text analysis to drive business logic.
- Personalized recommendation engines based on user profiles & preferences
- Text pattern & behavioral sentiment analytics models
- Advanced regression-based automated ranking tools
- Graph databases (Neo4j, Cypher) to reveal hidden insights in relational data
IoT & Hardware Automation
Physical prototypes integrating embedded devices, sensors, and actuators with intelligence at the edge.
- Raspberry Pi & Arduino firmware development
- Edge ML models optimized for low-resource processors via TensorFlow Lite
- Sensor integrations (ultrasonic, PIR, telemetry feeds)
- Actuator coordination (servos, mechanical switches, relays)
Data Specialist Consultations
End-to-end data pipeline setups for collecting, cleaning, and formatting large datasets for analytical platforms.
- Robust web-scraping agents with delay mapping to respect rules
- Structured DataFrame processing and cleaning pipelines
- API design & deployment using FastAPI, Streamlit, and Flask
- Data extraction, enrichment, and verification checks
Custom Requirements
If your project spans multiple domains or needs a specialized architecture, like edge ML, custom training pipelines, or full-stack dashboards, let's figure it out together.
Book ConsultationFrequently Asked
Sneha offers custom computer vision pipelines (image classification, deepfake detection, visual forensics), NLP and sentiment analysis systems, recommendation engines, IoT hardware prototyping with edge AI deployment, and end-to-end data pipeline consulting. All solutions are built with production-grade frameworks like PyTorch, TensorFlow, and FastAPI.
Yes. Sneha built DeepShield AI, a visual forensics pipeline achieving 97.5% accuracy on compressed GAN holdout sets. It uses Error Level Analysis, DCT frequency extraction, and a Vision Transformer (ViT) with Grad-CAM explainability. She also implements FGSM adversarial noise injection to proactively immunize photos against deepfakes.
Absolutely. Sneha engineers physical prototypes integrating Raspberry Pi and Arduino with edge ML models. Her GreenSort project runs quantized YOLOv8 on a Raspberry Pi 4 for real-time waste sorting with under 180ms loop latency. She handles sensor integration, serial communication, and actuator coordination.
Sneha is proficient in Python, Java, C/C++, JavaScript, TypeScript, and SQL. For AI/ML she uses PyTorch, TensorFlow, Keras, Scikit-Learn, and YOLO. For web development: Next.js, React, FastAPI, Flask, and Node.js. For databases: Neo4j, MongoDB, and MySQL. For hardware: Arduino and Raspberry Pi.
Yes. Sneha is currently open to internships, freelance projects, and contract collaborations in Computer Vision, Machine Learning, IoT prototyping, and Data Engineering. You can reach out through her contact page to discuss project requirements.
Sneha holds certifications from AWS (Machine Learning Foundations), Google Cloud (Data Engineer track), Neo4j (Certified Professional), IBM (Data Analysis with Python), and Cisco Networking Academy (IoT & Cybersecurity). All certifications are verifiable through her portfolio.