Ease-of-use analytics at scale is the holy grail of industrial strength machine learning. While there have been advances in APIs, algorithms, and user interfaces, building an end to end work flow involving data ingestion, data preparation, model training, model scoring, and visualization received limited investment and effort producing only marginal innovation. This paper outlines a proof of concept that demonstrates an analytical workflow that integrates multiple analytical tools and techniques for image recognition. The solution combines Relational Databases and Machine Learning (teradata), Deep Learning (TensorFlow), Distributed File System (HDFS), Graphical Processing Units, and user interface tools over a communication fabric (teradata QueryGrid). In particular, we demonstrate hand written word recognition through an application of Convolutional Neural Networks in TensorFlow and teradata's custom analytical functions to recognize first names and last names in the payee field section of financial negotiable instruments such as in a cheque. We hope that this paper will serve as a guide to successful implementation of analytical workflows in production.