Solving Data Challenges In Machine Learning With Automated Tools

Data is the lifeblood of machine learning (ML) projects. At the same time, the data preparation process is one of the main challenges that plague most projects. According to a recent study, data preparation tasks take more than 80% of the time spent on ML projects. Data scientists spend most of their time on data cleaning (25%), labeling (25%), augmentation (15%), aggregation (15%), and identification (5%). This article will talk about the most common data preparation challenges that require data scientists and machine learning engineers to spend so much time on data preparation. We'll also look at how self-service data preparation tools can help in overcoming these challenges.