A Machine Learning Engineer’s Must-Have Tools
I changed my career from SAP Consultant to Data Scientist roughly 4 years ago. After following a curriculum I designed for myself, I secured my Machine Learning Engineer role within a startup ecosystem in a year. It isn’t easy to condense all that I’ve learned, the tools I’ve used, and the experiences I’ve had during these past four years into a single article. Nonetheless, I’ll highlight the ones that have particularly benefited me. As I progressed in my role, I cultivated software development skills by using a variety of tools and following certain best practices on the job:Git is an open-source version control system widely used in software development. It organizes projects and manages collaboration between developers working on the same project. I was not using Git when I was working solo; I was manually versioning my code and notebooks 🙃When collaboration comes into play, Git becomes a necessity. It helps to track the progress of the project and fosters collaboration. It is a vast topic to learn from, and great sources are out there (like this one). Today, I will focus on the “commit” term and how it helped me organize my thought process.One of my first learnings in my early days was to have organized Git commits and concise commit messages. Later on, I realized that thinking beforehand about your commits and how to structure them also helps you to organize your work and design your code with a better logical pattern. Here is an example of how Git commits can be organized in the context of Data Science from one of my recent projects:
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