Service 1: Production-Ready ML Model Deployment

I will deploy your Machine Learning model to AWS or Azure
Best for: Startups who have a model in a Jupyter Notebook but don't know how to make it live
Turn your ML research into a functional and scalable web service. I take raw model files and convert them into production APIs that your frontend or mobile app can use.

What’s included:

API development using FastAPI or Flask
Cloud setup on AWS or Azure Web Apps based on your budget
Docker setup so the app runs the same in development and production
Basic API security and environment variable handling
Simple documentation for using the API

Service 2: Automated MLOps Pipelines (CI/CD)

I will build an automated CI/CD pipeline for your AI applications
Best for: Teams who want to stop manual deployments and reduce errors
I build automation pipelines that test, build, and deploy your code whenever you push to GitHub. This helps reduce downtime and avoids manual mistakes.

What’s included:

GitHub Actions workflows for automation
Basic automated testing setup
Docker registry setup (Docker Hub or AWS ECR)
Deployment updates without downtime
Optimized Docker builds for performance and size

Service 3: Model Experiment and Data Tracking

I will set up MLFlow and DVC for your Machine Learning lifecycle
Best for: Teams who want to track experiments and manage model versions properly
I set up tools to track your experiments, datasets, and models so everything is reproducible and organized.

What’s included:

MLFlow tracking server setup (local or cloud)
DVC setup for dataset versioning using S3 or Azure Blob
Model registry for managing versions
Setup to reproduce experiments easily