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I will build and deploy an automated machine learning pipeline with mlops on AWS
About this Gig
Gig Description:
"Are you struggling to move your Machine Learning models from a Jupyter Notebook to a live production environment? I will build a professional MLOps pipeline for you.
As an MLOps specialist, I bridge the gap between Data Science and Engineering. I don't just provide a script; I provide a scalable system that updates automatically.
What I offer:
- Model Deployment: FastAPI/Flask apps deployed on AWS EC2 or Lambda.
- Containerization: Dockerizing your ML app for consistent performance.
- Automation: Full CI/CD pipelines using GitHub Actions or AWS CodePipeline.
- Cloud Infrastructure: Setting up S3 for model artifacts and ECR for image storage.
- Database Integration: Connecting MongoDB for logging and real-time tracking.
Why choose me? I specialize in the 'production' side of AI, ensuring your model is reliable, fast, and easy to maintain."
Programming language:
Python
•
SQL
•
MLflow
Frameworks:
Scikit-learn
•
Panda
APIs:
Amazon Rekognition
Tools:
Jupyter Notebook
•
MLflow
FAQ
Do I need to provide the AWS account?
Yes, you will need to provide an AWS IAM user with limited permissions or allow me to guide you through the setup via Zoom.
Can you work with non-Python models?
I specialize in Python-based frameworks (Scikit-Learn, PyTorch, TensorFlow), but I can containerize almost any environment.
