You will get MLOps pipelines to deploy and monitor ML models
Rising Talent
Project details
Transform your Machine Learning models from experimental Notebooks into robust, production-ready systems.
Many data science projects fail because they never leave the Jupyter Notebook. I bridge the critical gap between Data Science and Operations (MLOps). I don't just "run" your code; I build an automated ecosystem around it ensuring scalability, reliability, and ease of maintenance.
How I can help you:
Model Deployment: I will wrap your model in Docker containers and deploy it as a high-performance REST API (using FastAPI or Flask) on the cloud.
Automated Pipelines (CI/CD): Stop manually moving files. I set up pipelines (GitHub Actions/Jenkins) so that every time you update code or data, your model automatically retrains and redeploys.
Cloud Infrastructure: Whether you use AWS (SageMaker, Lambda, EC2), Google Cloud, or Azure, I configure the environment securely.
Monitoring: I implement tools to track model health, latency, and data drift, ensuring your AI keeps performing well over time.
Why choose this gig?
Clean, modular code structure.
Security best practices (API Keys/IAM Roles).
Documentation included for easy handover.
Let’s automate your AI workflow today.
Many data science projects fail because they never leave the Jupyter Notebook. I bridge the critical gap between Data Science and Operations (MLOps). I don't just "run" your code; I build an automated ecosystem around it ensuring scalability, reliability, and ease of maintenance.
How I can help you:
Model Deployment: I will wrap your model in Docker containers and deploy it as a high-performance REST API (using FastAPI or Flask) on the cloud.
Automated Pipelines (CI/CD): Stop manually moving files. I set up pipelines (GitHub Actions/Jenkins) so that every time you update code or data, your model automatically retrains and redeploys.
Cloud Infrastructure: Whether you use AWS (SageMaker, Lambda, EC2), Google Cloud, or Azure, I configure the environment securely.
Monitoring: I implement tools to track model health, latency, and data drift, ensuring your AI keeps performing well over time.
Why choose this gig?
Clean, modular code structure.
Security best practices (API Keys/IAM Roles).
Documentation included for easy handover.
Let’s automate your AI workflow today.
AI Development Type
Deep Learning, Model Tuning, Software MaintenanceAI Tools
Amazon SageMaker, Apache MXNet, deeplearn.js, Deeplearning4j, Keras, MLflow, OpenCV, PyTorch, Sonnet, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$200
|
Standard
$750
|
Advanced
$2,000
|
|---|---|---|---|
| Delivery Time | 4 days | 10 days | 20 days |
Number of Revisions | 1 | 2 | 9 |
AI Model Integration | |||
Detailed Code Comments | - | ||
Knowledge Graph | - | - | - |
Model Documentation | - | ||
Ontology | - | - | - |
Source Code | |||
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$100 - $300
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RS
Ramon S.
Apr 17, 2026
AI Engineer for Daily Widget Development
I would highly reccomend using Iqra for your projects! She was fast and professional.
About Iqra
AI & Machine Learning |
Chiniot, Pakistan - 5:52 am local time
Steps for completing your project
After purchasing the project, send requirements so Iqra can start the project.
Delivery time starts when Iqra receives requirements from you.
Iqra works on your project following the steps below.
Revisions may occur after the delivery date.
Containerization (Docker)
I will clean your code, create a requirements.txt, and wrap your model in a Docker container to ensure it runs consistently across all environments.
CI/CD Pipeline Setup
I will configure the automation workflow (using GitHub Actions or Jenkins). This ensures that whenever you push code, the model is auto-tested and built.