Apache Spark Mllib Vs Scikit-learn: Building Machine Learning Pipelines

This article discusses the various tasks which need to be completed in order to successfully implement a machine learning pipeline in a real-life setting. It explains how using libraries such as Pandas, NumPy, NLTK, and SpaCy can help with the data preparation steps. Additionally, the article explains the importance of using a specially designed framework in order to maintain code consistency and reduce the chances of human error.