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Revenue forecasting is required by most enterprises for strategic business planning and for providing expected future results to investors. However, revenue forecasting processes in most companies are time-consuming and error-prone as they are performed manually by hundreds of financial analysts. In this paper, we present a novel machine learning based revenue forecasting solution that we developed to forecast 100% of Microsoft's revenue (around $85 Billion in 2016), and is now deployed into production as an end-to-end automated and secure pipeline in Azure. Our solution combines historical trend and seasonal patterns with additional information, e.g., sales pipeline data, within a unified modeling framework. In this paper, we describe our framework including the features, method for hyperparameters tuning of ML models using time series cross-validation, and generation of prediction intervals. We also describe how we architected an end-to-end secure and automated revenue forecasting solution on Azure using Cortana Intelligence Suite. Over consecutive quarters, our machine learning models have continuously produced forecasts with an average accuracy of 98-99 percent for various divisions within Microsoft's Finance organization. As a result, our models have been widely adopted by them and are now an integral part of Microsoft's most important forecasting processes, from providing Wall Street guidance to managing global sales performance.