Practical Data Science - How to Track Your Development Process with DVC


Datacentric applications utilising machine learning models have evolved into common solutions. Many projects however still suffer from a lack of good patterns and practices, when developing such powerful technologies. Digging down into the nitty-gritty details, we explain how you can use DVC to version all parts of your projects: From the dataset, over gluecode up to the model itself. But wait, there’s more! We show you code that covers the full development cycle, including experiments and reproducability, as well as release and deployment of your model to machines in the wild.

Vilnius, Lithuania