You will get an automated image classification or medical imaging analysis pipeline
Rising Talent
Rising Talent
Project details
I build automated image classification pipelines for medical imaging, agriculture, and industrial use cases — with explainability built in so you can trust the predictions.
I've built a retinal disease classifier across 3 datasets using domain generalization, and a betel leaf disease detector with Grad-CAM visual explanations. Both are live and deployed. I work with PyTorch and TensorFlow, and I include confusion matrices, accuracy reports, and Grad-CAM heatmaps as standard.
This is for you if you need a model that classifies images reliably — and you need to explain why it made each decision.
I've built a retinal disease classifier across 3 datasets using domain generalization, and a betel leaf disease detector with Grad-CAM visual explanations. Both are live and deployed. I work with PyTorch and TensorFlow, and I include confusion matrices, accuracy reports, and Grad-CAM heatmaps as standard.
This is for you if you need a model that classifies images reliably — and you need to explain why it made each decision.
AI Algorithms
AdaBoost, Convolutional Neural Network, Deep Belief Network, Feedforward Neural Network, Generative Adversarial Network, Large Language Model, Multimodal Large Language Model, Regression Analysis, Transformer Model, YOLOAI Applications
AI Text-to-Image, AI-Enhanced Medical Imaging, Anomaly Detection, Facial Recognition, Image Analysis, Image Processing, Image RecognitionAI Development Language
PythonAI Tools
Bing AI, Copy.ai, GitHub Copilot, Hugging Face, NVIDIA AI Platform, PyTorch, Replit, Streamlit, TensorFlowAI Models
BERT, ChatGPT, GPT-3, GPT-4, Midjourney AI, Naive Bayes Classifier, OpenAI CodexWhat's included
| Service Tiers |
Starter
$180
|
Standard
$220
|
Advanced
$350
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 10 days |
Number of Revisions | 2 | 3 | 6 |
AI Model Integration | |||
Batch Normalization | |||
Database Integration | - | ||
Detailed Code Comments | - | - | |
Image Upscaling | - | ||
MLOps | - | - | - |
Model Deployment | |||
Model Documentation | - | ||
Model Monitoring | - | - | - |
Model Testing & Optimization | - | ||
Model Tuning | - | - | |
Natural Language Processing | - | ||
NLP Tokenization | - | - | |
Pre-Training | |||
Prompt Engineering | - | ||
Setup File | - | - | |
Source Code |
About Shanzia Shabnom
ML Engineer & AI Technical Writer - LLMs, RAG, Computer Vision
Dhaka, Bangladesh - 9:23 pm local time
engineers actually trust.
ML Engineering Background:
- Computer Vision: PyTorch, OpenCV, GradCAM, domain generalization
- LLMs & RAG: LangChain, FAISS, Ollama, vector search
- Production ML: FastAPI, Docker, Streamlit, PostgreSQL
- Agentic Workflows: LLM auditing, search validation at 200K+ scale
I'm a regular contributor to Towards AI with over a year of active technical writing experience covering topics I've actually built and deployed. High-volume writing background with consistent delivery under deadline.
Every piece is backed by working code.
Open to a paid sample article to start.
Response time: 0–4 hours.
Steps for completing your project
After purchasing the project, send requirements so Shanzia Shabnom can start the project.
Delivery time starts when Shanzia Shabnom receives requirements from you.
Shanzia Shabnom works on your project following the steps below.
Revisions may occur after the delivery date.
Dataset review and preprocessing
Model selection and training (CNN / transfer learning)