You will get an end-to-end ML pipeline with Docker deployment
Project details
I am a Machine Learning Engineer specializing in building end-to-end ML pipelines with production-grade deployment. I will take your raw data and deliver a fully working ML system from data ingestion and validation to model training, Docker containerization, and live deployment. I have built and deployed multiple ML projects on AWS and Render, including a phishing detection system with 99.1% F1-score. You get clean, modular, well-documented code that is ready for production.
Machine Learning Tools
ChatGPT, GitHub Copilot, GPT-3, MLflow, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, Sonnet, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$25
|
Standard
$60
|
Advanced
$120
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 3 | 5 | 8 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$10 - $20
Additional Revision
+$5Frequently asked questions
About Kunal
Machine Learning Engineer | MLOps & Model Deployment | AWS & Docker
Alwar, India - 11:56 pm local time
I specialize in taking Machine Learning models out of the notebook and into production. As a B.Tech student focused on AI and Data Science, I focus on the entire ML lifecycle—from data cleaning and model training to containerization and deployment.
What I Do:
• Model Development: Building robust models for classification, regression, and Computer Vision (CNNs/LSTMs).
• MLOps Foundations: Using Docker and MLflow to version models and ensure they run anywhere.
• Production-First Approach: I build systems with deployment in mind, using FastAPI to serve models as APIs.
Recent Projects:
• End-to-End Network Security IDS: Developed a system to detect network intrusions, featuring a full MLOps pipeline with Docker and MLflow for experiment tracking.
• Real-Time Sign Language Interpreter: Built a Computer Vision application using MediaPipe and LSTM models for high-accuracy gesture recognition.
My Technical Stack:
• Languages: Python (Pandas, NumPy, Scikit-Learn), SQL.
• ML Tools: TensorFlow, Keras, MediaPipe.
• MLOps & DevOps: Docker, MLflow, DagsHub, GitHub.
• Cloud: Basic deployment experience on AWS and Azure.
I am looking to help startups and small businesses automate their data workflows and deploy their first ML models. If you need a detail-oriented developer who understands both the math and the deployment, let's talk!
Steps for completing your project
After purchasing the project, send requirements so Kunal can start the project.
Delivery time starts when Kunal receives requirements from you.
Kunal works on your project following the steps below.
Revisions may occur after the delivery date.
Analyze your dataset and define the problem
Data ingestion, cleaning and validation