Story

For a couple of months, I have been creating single components of a Machine Learning Platform at my work. I thought that creating a similar solution as a whole would be a good enough challenge for me for private repositories contributions. That’s when I decided to create a Fullstack MLOps Platform for Formula 1 – a coherent place, where any petrolhead can experience the true queen of motorsport.

Solution

I decided that the whole F1 Platform will be run with the usage of minikube Kubernetes distribution, together with Docker, Python and Git codebase. For single components, I have used a vast number of popular frameworks, used everyday in the data science and Machine Learning world, for instance: MLFlow, MinIO, Dash, PyGWalker, Prometheus, Grafana, Roboflow.

Platform Architecture

Landscape view

Components

Project Phases

• 1. Locally on PC,
• 2. On-premise VPS,
• 3. Cloud-based:
  • 1. Google Cloud Platform,
  • 2. Amazon Web Services,
  • 3. Microsoft Azure.

Lesson Learned

By contributing to this project, I have observed that making hands-on engineering is the best way of learning for me. By: doing something valuable, having fun myself, and proving Hiring Managers wrong – creating this Fullstack MLOps Platform has been a very fulfilling code-journey for me. Moreover, not only has it enabled me to give back to the community, but also has it promoted Formula One to interested parties in a way (especially: business end-users and MLOps Engineers).