| Popis: |
Ease-of-use analytics at scale is the holy grail of industrial strength machine learning. In order to reap benefits from the mother-lode of business related data; tools, technologies, and analytical functions should operate in perpetual symphony to derive insightful business outcomes. While there have been advances in APIs, algorithms, and user interfaces, building an end to end workflow spanning data ingestion, data preparation, model training, model scoring, visualization and finally continuous improvement and model management received limited investment. In this paper we demonstrate an analytical workflow that integrates multiple analytical tools and techniques for image recognition wrapped in the model management framework. As analytics in industry is maturing, analytics implementations are no longer one-off, but are components of Analytics Operations known as AnalyticsOps. We introduce the notion of Model Quality Index (MQI) to track model performance. The MQI is similar to Process Capability Index (PCI) common in $6 \sigma$ programs in manufacturing. Our solution combines relational databases (Teradata DB), Machine Learning (Teradata/Aster), Deep Learning (TensorFlow), Hadoop Distributed File System (HDFS), and user interface tools over a communication fabric (Teradata QueryGrid). In particular, we demonstrate a hand written word recognition use-case for an enterprise customer cast in a model management workflow for repeatable deployments across a range of businesses. |
