Agile data science research is hard, how can you give time estimation when you are not sure that your problem is solvable? How can you plan your sprint before looking at the data? You probably can’t. Agile data science requires many adjustments. In this post, I am going to share some of the best practices that work best for agile data science research. Every machine learning project should start by defining the goals of the project.
As a data scientist, one of your most important skills should be choosing the right modeling techniques and algorithms for your problems. You need a model that will use a deeper semantic understanding of the documents. Deep learning with small data is still in its early stages as a research field but it looks like it’s gaining more popularity especially with pre-trained language models and hope that researchers and practitioners will find more methods that will make deep learning valuable to every dataset.