You will get a predictive machine learning pipeline with FastApi
Project details
Are you looking to deploy, automate, and scale your Machine Learning projects?
I help businesses and startups transform ML models into production-ready systems using modern MLOps practices. From data versioning and experiment tracking, I build reliable and maintainable machine learning infrastructure.
Services Include:
• End-to-End MLOps Pipelines
• MLflow Experiment Tracking
• DVC Data & Model Versioning
• Docker Containerization
• FastAPI Model Deployment
• GitHub Actions
• Model Monitoring & Automation
• Machine Learning API Development
• RAG Application Deployment
Tech Stack:
Python, FastAPI, Docker, MLflow, DVC, GitHub Actions, TensorFlow, PyTorch, SQL
Why Choose Me?
✓ Clean and maintainable code
✓ Industry-standard MLOps practices
✓ Detailed documentation
✓ Fast communication and support
Let's discuss your project requirements and build a production-ready ML system.
I help businesses and startups transform ML models into production-ready systems using modern MLOps practices. From data versioning and experiment tracking, I build reliable and maintainable machine learning infrastructure.
Services Include:
• End-to-End MLOps Pipelines
• MLflow Experiment Tracking
• DVC Data & Model Versioning
• Docker Containerization
• FastAPI Model Deployment
• GitHub Actions
• Model Monitoring & Automation
• Machine Learning API Development
• RAG Application Deployment
Tech Stack:
Python, FastAPI, Docker, MLflow, DVC, GitHub Actions, TensorFlow, PyTorch, SQL
Why Choose Me?
✓ Clean and maintainable code
✓ Industry-standard MLOps practices
✓ Detailed documentation
✓ Fast communication and support
Let's discuss your project requirements and build a production-ready ML system.
Machine Learning Tools
ChatGPT, GitHub Copilot, Keras, MLflow, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, scikit-learn, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$10
|
Standard
$20
|
Advanced
$50
|
|---|---|---|---|
| Delivery Time | 1 day | 2 days | 4 days |
Number of Revisions | 2 | 3 | Unlimited |
Model Validation/Testing | - | - | |
Model Documentation | - | ||
Data Source Connectivity | - | - | - |
Source Code |
About Arhum
Machine Learning & MLOps Engineer | Python | ML Pipeline
Gilgit, Pakistan - 2:03 pm 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.