Using a tool
Using tools specifically made for one part of the machine learning lifecycle can save you a lot of time and money. Even though you could possibly build these tools yourself, it often does not weigh up against the cost of using the tool.
Imagine you are working in a team of data scientists, and each of you has produced code for the development of the machine learning model on your local computer. Because the team just started out, they just shared the files on the network file system, but it's starting to become difficult to work together on the same code.
Which tool should you use to be able to collaborate and share a code repository?
This exercise is part of the course
MLOps Concepts
Hands-on interactive exercise
Turn theory into action with one of our interactive exercises
