You will get Deploy Machine Learning Model as Interactive Web App (Streamlit / Gradio)
Project details
I will deploy your Machine Learning model as an interactive, user-friendly web application using Streamlit or Gradio. This will allow users to easily interact with your model, visualize results, and make predictions in real-time. With hands-on experience in Python, ML frameworks, and web app deployment, I ensure your project is fully functional, responsive, and ready for end-users. The final deliverable includes a polished web interface, backend integration, and detailed instructions for running or hosting your app.
What I Will Do
Convert your ML / DL model into a web app
Build clean UI using Streamlit or Gradio
Enable file upload / input forms
Display predictions & results clearly
Deploy on Streamlit Cloud or Hugging Face
What This Project Is Perfect For
Image classification models
Resume screening / NLP models
Prediction models (regression / classification)
Academic or startup ML projects
Tech Stack
Python
Streamlit / Gradio
Scikit-learn / TensorFlow / Keras
Pandas, NumPy
Deliverables
Fully working web app
Source code (GitHub)
Deployment link
Setup instructions
What I Will Do
Convert your ML / DL model into a web app
Build clean UI using Streamlit or Gradio
Enable file upload / input forms
Display predictions & results clearly
Deploy on Streamlit Cloud or Hugging Face
What This Project Is Perfect For
Image classification models
Resume screening / NLP models
Prediction models (regression / classification)
Academic or startup ML projects
Tech Stack
Python
Streamlit / Gradio
Scikit-learn / TensorFlow / Keras
Pandas, NumPy
Deliverables
Fully working web app
Source code (GitHub)
Deployment link
Setup instructions
What's included
| Service Tiers |
Starter
$20
|
Standard
$25
|
Advanced
$35
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 1 | 2 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 0 | 2 | 4 |
Model Validation/Testing | - | ||
Model Documentation | - | - | |
Data Source Connectivity | - | - | |
Source Code |
About Swathi
Entry-Level Machine Learning Engineer | Data Analyst
Amalapuram, India - 9:07 pm local time
Iam a Machine Learning & AI Engineer with hands-on experience building real, production-ready AI solutions using Python, Deep Learning, NLP, Transformers, and LLM-based pipelines. I specialize in creating intelligent systems that turn raw data into accurate predictions, insights, and automation.
My work includes building end-to-end ML applications, optimizing deep learning models, creating semantic search & embedding-based systems, and deploying user-friendly AI tools using Streamlit, Gradio, and HuggingFace Spaces.
What I Can Build for You:
Custom Machine Learning models (classification, prediction, clustering, optimization)
NLP solutions (resume parsing, semantic search, text extraction, summarization)
LLM-based pipelines using Transformers, Sentence-BERT, RAG, FAISS
Deep learning models (CNNs, image classification, benchmarking)
AI-powered dashboards & web apps (Streamlit/Gradio)
Automations using Python, APIs, and data workflows
Complete end-to-end AI product development
Key Highlights:
Improved deep learning model performance by 60% and reduced inference latency by 62%
Built HireMatch-AI, a semantic resume-job matching platform using embeddings
Built & deployed projects on HuggingFace Spaces with live demos
Experienced with TensorFlow, Keras, Scikit-learn, Transformers, NumPy, Pandas, Plotly
I’m detail-oriented, fast-learning, and committed to delivering clean, reliable, and scalable AI solutions. Whether you need a small script, a custom model, or a complete AI workflow — I can help you build it efficiently.
Let’s work together to bring your AI idea to life!
Steps for completing your project
After purchasing the project, send requirements so Swathi can start the project.
Delivery time starts when Swathi receives requirements from you.
Swathi works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Gathering
You provide the ML model, dataset (if needed), and any specific app functionality or design preferences.
App Development
I create an interactive web app using Streamlit or Gradio, integrating your ML model seamlessly.