You will get a custom machine learning model in python
Project details
I develop custom machine learning solutions tailored to your business needs. From data preprocessing and feature engineering to model training, evaluation, and deployment, I deliver accurate, scalable, and well-documented ML models using Python and industry best practices. Whether you need classification, regression, forecasting, or predictive analytics, I focus on building reliable solutions that generate real business value.
Machine Learning Tools
ChatGPT, GitHub Copilot, Keras, MLflow, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, scikit-learn, SQL, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$20
|
Standard
$40
|
Advanced
$100
|
|---|---|---|---|
| Delivery Time | 3 days | 4 days | 5 days |
Number of Revisions | 2 | 3 | Unlimited |
Model Validation/Testing | - | - | |
Model Documentation | - | ||
Data Source Connectivity | - | - | - |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$10 - $15Frequently asked questions
About Arhum
Machine Learning & MLOps Engineer | Python | ML Pipeline
Gilgit, Pakistan - 5:39 am local time
My expertise lies in Machine Learning Operations (MLOps), Machine Learning Engineering, and AI Backend Development, with a strong focus on transforming machine learning models into reliable and maintainable products.
I specialize in designing end-to-end ML pipelines, data and model versioning, experiment tracking, model deployment, and automated CI/CD workflows. I have hands-on experience with Python, FastAPI, Docker, DVC, MLflow, GitHub Actions, and cloud technologies, enabling me to build robust machine learning solutions from development to production.
My work includes developing machine learning pipelines, deploying AI-powered APIs, implementing model monitoring and versioning strategies, and building intelligent applications such as computer vision systems, RAG-based chatbots, and AI-driven backend services.
Core Competencies:
• Machine Learning Operations (MLOps)
• Machine Learning Engineering
• Python Development
• FastAPI & REST APIs
• Data & Model Versioning (DVC)
• Experiment Tracking (MLflow)
• Docker & Containerization
• CI/CD Automation
• AI Backend Systems
I am continuously learning and building innovative AI solutions while seeking opportunities to contribute as an AI Engineer, MLOps Engineer, or Machine Learning Engineer in organizations that value scalable and impactful AI systems.
Steps for completing your project
After purchasing the project, send requirements so Arhum can start the project.
Delivery time starts when Arhum receives requirements from you.
Arhum works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements Analysis
Review project requirements, existing code, models, datasets, and deployment goals.
Solution Design
Define the development architecture, technology stack, and implementation plan.