You will get ML model deployed with CI/CD, monitoring, automated retraining using MLOps
Rising Talent
Rising Talent
Project details
Is your trained model stuck in a notebook while your business waits? Deployment, monitoring, and retraining are where most ML projects fail.
Using MLflow, Docker, AWS, and GitHub Actions, I will take your model and turn it into a production-grade ML system your team can call via API, monitor in real time, and retrain automatically.
What You Get
➤ Model containerisation using Docker and FastAPI REST API
➤ MLflow experiment tracking and model registry
➤ CI/CD pipeline: automated testing, validation, and deployment
➤ AWS deployment: EC2, SageMaker, Lambda, or ECS
➤ Data versioning with DVC for full reproducibility
➤ Drift detection and performance monitoring
➤ Automated retraining pipeline on new data or performance drop
➤ Infrastructure as code with Terraform
Why Work With Me
➤ Full-stack MLOps: model packaging to cloud deployment
➤ Real AWS production deployments using SageMaker and EC2
➤ CI/CD pipelines that redeploy automatically on every update
➤ Clean documented infrastructure code your team can maintain
Stop letting models collect dust in notebooks. Let me deploy yours so it delivers real business value every single day.
Happy Day!
Using MLflow, Docker, AWS, and GitHub Actions, I will take your model and turn it into a production-grade ML system your team can call via API, monitor in real time, and retrain automatically.
What You Get
➤ Model containerisation using Docker and FastAPI REST API
➤ MLflow experiment tracking and model registry
➤ CI/CD pipeline: automated testing, validation, and deployment
➤ AWS deployment: EC2, SageMaker, Lambda, or ECS
➤ Data versioning with DVC for full reproducibility
➤ Drift detection and performance monitoring
➤ Automated retraining pipeline on new data or performance drop
➤ Infrastructure as code with Terraform
Why Work With Me
➤ Full-stack MLOps: model packaging to cloud deployment
➤ Real AWS production deployments using SageMaker and EC2
➤ CI/CD pipelines that redeploy automatically on every update
➤ Clean documented infrastructure code your team can maintain
Stop letting models collect dust in notebooks. Let me deploy yours so it delivers real business value every single day.
Happy Day!
AI Development Type
Deep Learning, Software MaintenanceAI Tools
Amazon SageMaker, Azure Machine Learning, Keras, MLflow, OpenCV, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$400
|
Standard
$700
|
Advanced
$1,300
|
|---|---|---|---|
| Delivery Time | 7 days | 15 days | 24 days |
Number of Revisions | 1 | 2 | 2 |
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 - $400
Additional Revision
+$60Frequently asked questions
About Muhammad
AI Engineer | LLM Apps, RAG, AI Agents, Web and Mobile Development
Rohillanwali, Pakistan - 1:16 am local time
I love understanding how things actually work and turning ideas into reliable AI systems that save time, cut costs, and make businesses run smarter.
Here is what I can build for you:
✦ AI Agents and Automation
Autonomous agents that handle your workflows, customer support, research, data processing, and daily business operations on their own.
✦ LLM Apps and Chatbots
Smart chatbots and assistants built on Claude, OpenAI, or Gemini that answer questions, process documents, and talk to your customers 24/7.
✦ RAG Systems and Document Intelligence
Upload your documents, manuals, or databases and let the AI answer questions instantly and accurately.
✦ Web Development
Full SaaS platforms, business dashboards, landing pages, and REST APIs built with Next.js, React, and FastAPI.
✦ Mobile App Development
iOS and Android apps built with Flutter or React Native, with AI features included from day one.
✦ MLOps and Cloud Deployment
I deploy and maintain your AI systems on AWS and Docker so they run reliably without downtime.
Industries I have worked in:
· Cybersecurity · Healthcare · Legal · Finance
· E-Commerce · Real Estate · Food & Restaurant · Mining
Tech I use:
LLMs › Claude, OpenAI, Gemini, LLaMA, Mistral
Agents › LangChain, LangGraph, CrewAI, Anthropic SDK
RAG › FAISS, Pinecone, ChromaDB, pgvector
Backend › FastAPI, Node.js, NestJS
Frontend › Next.js, React, Tailwind
Mobile › Flutter, React Native
Cloud › AWS, Docker, Terraform
MLOps › MLflow, DVC, GitHub Actions, BitBucket, Gitlab
Real things I have shipped:
▸ An AI system that reads live security alerts and writes full investigation reports automatically, used in a real SOC environment.
▸ A RAG chatbot inside a mobile food app that answers menu questions, tracks preferences, and updates in real time.
▸ An AI agent that finds job listings, extracts HR emails, and sends personalized applications automatically, 50+ per day.
I respond fast. I communicate clearly. I deliver on time.
If you have a project in mind, even just a rough idea, message me and I will tell you exactly how I can build it for you.
Let’s build something great together.
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Audit & Architecture
I review your trained model, infrastructure requirements, and scale needs. I recommend the right deployment stack and agree on monitoring thresholds before writing any code.
Build & Automate
I containerise the model, build the API, set up the CI/CD pipeline, configure MLflow tracking, and deploy to your cloud environment with full testing.