ML-DevOps Pipeline Project
Budget: $250 – $750 AUD
This project aims to build a development environment for data scientists and machine learning experts.
The pipeline will include the following tools integrated smoothly:
1. Jupyter hub as IDE
2. Github or Git lab for source control
3. Jenkins or Kubeflow for orchestration
4. Docker for containerization
5. Kubernetes for scaling
6. Gunicorn + Flask for hosting
7. Nginx as web proxy
I attached 2 pictures to describe the simplified architecture, just to describe the idea.
This project will be hosted on Linux (likely to be Debian)
I will provide the machine for initial work, you will deliver
1. A script that installs and configures all the tools smoothly to build the whole environment on a bare machine
2. Ideally, running the code on my machine (bare metal) will make the environment up and running and usable for our data scientists and machine learning experts
The pipeline will include the following tools integrated smoothly:
1. Jupyter hub as IDE
2. Github or Git lab for source control
3. Jenkins or Kubeflow for orchestration
4. Docker for containerization
5. Kubernetes for scaling
6. Gunicorn + Flask for hosting
7. Nginx as web proxy
I attached 2 pictures to describe the simplified architecture, just to describe the idea.
This project will be hosted on Linux (likely to be Debian)
I will provide the machine for initial work, you will deliver
1. A script that installs and configures all the tools smoothly to build the whole environment on a bare machine
2. Ideally, running the code on my machine (bare metal) will make the environment up and running and usable for our data scientists and machine learning experts