Disclaimer: This is my personal Medium blog, therefore anything I post, share, and comment don't reflect my employer. When we started exploring Machine Learning, we realized that there were many solutions available in the market from concept to production. To simplify and understand the options, our initial approach has been, understanding of concepts from MOOC's, hands on coding using classical ML packages like Numpy, Pandas, Scikit Learn, NLTK, Matplotlib…mostly using Python/R on Anaconda/Pycharm/Rstudio as IDE Though it was easy to get hands dirty & see the power of ML, it became complex when the classical ML approach were deployed for real world problems. Problems compounded due to many factors such as availability /quality of training data, imbalanced data sets, feature engineering requirements, lower precision, accuracy…. When we were building a Machine learning-based classifier for a NLP Classification problem for a business requirement, we encountered challenges in the form of Feature Selection, Imbalanced Data Set, hyper parameter Tuning, etc.