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This book covers topics such as data management techniques, building models using different machine learning (ML) algorithms and deployment methods, in relation to building end-to-end machine learning pipeline. Training ML models in a standalone environment for research or academic purpose is very different than training models and moving them into production such that products across enterprise could invoke them for predictions. In the enterprise setting, there is a need for data science, product engineering and IT teams to collaborate with each other for understanding the problem, gather data from different product data sources, train and test different models, move the best model in production and finally monitor the models at regular intervals. All this requires one to have a good understanding of different aspects of ML model development lifecycle. This is where this book will come handy for different stakeholders including data scientists, product managers, IT staff and business leaders.