ML Workflow for Research Scientists
Chapter 1 Preamble
Who is this wiki for?
There is a long list of Python and Github workflow tutorials. There are so many Machine Learning Tutorials too. However, there is no one easy to follow Machine Learning-ML Cloud Service-Local Development-Benchmarking Workflow. I am going to talk about three Free services which any person can use with basic internet access - Google Colaboratory (for ML Cloud Service), VS Code (Local ML Development) and Weights and Biases (for benchmarking). TL;DR- This is a process guide for deploying your ML models as a researcher!
What won’t you learn?
How to code in Python or develop Machine Leaning Models or learn Pytorch.
What will you learn?
- How to contribute code to a well-structured projects
- Work with multiple Google Colaboratory Notebooks
- Create easy Python Documentation
- Scale up as your project and team grows in complexity and size
- Benchmark and Project Tracking