TY - GEN
T1 - Enterprise-wide Machine Learning using Teradata Vantage
T2 - 2019 IEEE International Conference on Big Data, Big Data 2019
AU - Lakshminarayan, Choudur
AU - Ramakrishnan, Thiagarajan
AU - Al-Omari, Awny
AU - Bouaziz, Khaled
AU - Ahmad, Faraz
AU - Raghavan, Sri
AU - Agarwal, Prama
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Big data characterized by variety can be divided into 3 principal categories: numeric structured data, semi-structured data, and unstructured multimedia data involving audio, video, and text. Decision making requires multiple analytical engines suitable for each type of data, programming languages, algorithms, visualization tools, and user interfaces. More often than not, industrial analytics is conducted ad hoc by lashing together analytics components such as distributed data sources, analytics engines, and algorithms. This kind of piecemeal approach ignores scale, security, governance, reliability, model management and fault tolerance that are paramount for industrial strength analytics. A unified, versatile, and robust architecture that combines various components in a single integrated platform is the need of the hour. Teradata Vantage (TD Vantage) is such a platform for delivering production quality enterprise analytics at scale. In this paper, we outline the proposed TD Vantage (available in the market and under continuous development) that unifies data, engines, and algorithms operating in a seamless symphony. We will demonstrate its capabilities through three proofs of concept biz: image data using TensorFlow, text data using Spark, and transaction data using Aster (now renamed Machine Learning Engine or MLE), with Teradata orchestrating interactions among the various components.
AB - Big data characterized by variety can be divided into 3 principal categories: numeric structured data, semi-structured data, and unstructured multimedia data involving audio, video, and text. Decision making requires multiple analytical engines suitable for each type of data, programming languages, algorithms, visualization tools, and user interfaces. More often than not, industrial analytics is conducted ad hoc by lashing together analytics components such as distributed data sources, analytics engines, and algorithms. This kind of piecemeal approach ignores scale, security, governance, reliability, model management and fault tolerance that are paramount for industrial strength analytics. A unified, versatile, and robust architecture that combines various components in a single integrated platform is the need of the hour. Teradata Vantage (TD Vantage) is such a platform for delivering production quality enterprise analytics at scale. In this paper, we outline the proposed TD Vantage (available in the market and under continuous development) that unifies data, engines, and algorithms operating in a seamless symphony. We will demonstrate its capabilities through three proofs of concept biz: image data using TensorFlow, text data using Spark, and transaction data using Aster (now renamed Machine Learning Engine or MLE), with Teradata orchestrating interactions among the various components.
KW - Analytics Engines
KW - Artificial Intelligence
KW - Image Recognition
KW - Integrated Analytics Platform
KW - Machine Learning
KW - Text Analysis by NLP
UR - http://www.scopus.com/inward/record.url?scp=85081411837&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85081411837&partnerID=8YFLogxK
U2 - 10.1109/BigData47090.2019.9006321
DO - 10.1109/BigData47090.2019.9006321
M3 - Conference contribution
AN - SCOPUS:85081411837
T3 - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
SP - 2043
EP - 2046
BT - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
A2 - Baru, Chaitanya
A2 - Huan, Jun
A2 - Khan, Latifur
A2 - Hu, Xiaohua Tony
A2 - Ak, Ronay
A2 - Tian, Yuanyuan
A2 - Barga, Roger
A2 - Zaniolo, Carlo
A2 - Lee, Kisung
A2 - Ye, Yanfang Fanny
Y2 - 9 December 2019 through 12 December 2019
ER -