Engineering the Future of Software
Feb 25–26, 2018: Training
Feb 26–28, 2018: Tutorials & Conference
New York, NY

Architecting an advanced analytics platform for machine learning

Georgios Gkekas (ING Bank)
3:50pm–4:40pm Tuesday, February 27, 2018
Secondary topics:  Best Practice, Case Study
Average rating: ***..
(3.33, 9 ratings)

Who is this presentation for?

  • Architects, tech leads, and senior developers

Prerequisite knowledge

  • Familiarity with the concepts of big data and basic data science workflows

What you'll learn

  • Explore ING's central, best-of-breed technical advanced analytics platform tailored for data science activities
  • Understand how DevOps and CI tooling can accelerate rapid prototyping within big data and analytics


As data increasingly becomes the new currency, the “triple A” concept of advanced analytics, automation, and AI has taken a central role within today’s enterprises. Modern corporations must make intelligent use of data—both their own and from third parties—if they want to ensure sustainable growth in the future and that they understand and are meeting customer needs. Currently, a myriad of data processing frameworks promise the "holy grail” of getting data insights into the executive suite. However, despite the abundance of frameworks, the effective integration of those frameworks into production environments, especially in highly regulated markets such as banking, still remains a huge challenge. The big data space has focused too much on covering the functional needs of the industry and has neglected the equally important nonfunctional ones, such as security, disaster recovery, regulatory compliance, and data backups, to name a few. Additionally, many companies struggle to find the right balance between the breadth of use cases covered by modern technologies and wide acceptance from CI/BI professionals. More often than not, executives, architects, and tech leads find themselves overwhelmed by the abundance of offerings and spend a substantial amount of time finding experts to use those technologies—not to mention how challenging it is to promote such a tech stack to a traditional in-house analytics team.

Georgios Gkekas shares ING’s advanced analytics journey to promote modern machine and deep learning techniques internally through a central, best-of-breed technical platform, tailored for data science activities. This is easier said than done in a federated bank with worldwide branches and distributed and decentralized data sources. Georgios offers an overview of the platform, which facilitates data scientists’ daily work by offering only the necessary automated tools to replace the tedious, repetitive, and error-prone steps in a typical data science pipeline. He also demonstrates how to create a ubiquitous big data platform that

  • Offers easy integration of data sources via APIs,
  • Contains microservices as key components of the self-service offering,
  • Implements a layered but at the same time easy and intuitive data architecture,
  • Applies DevOps and CI practices in data science model deployment and execution,
  • Offers an attractive platform to be used by even traditional data analysts,
  • Integrates with enterprise authentication and authorization security mechanisms,
  • Is ready for integration with the enterprise data lake.
Photo of Georgios Gkekas

Georgios Gkekas

ING Bank

Georgios Gkekas is a big data architect on the international advanced analytics team at ING Bank, where he helps various business units gain insights from their data through ING’s advanced analytics big data environment and is working to extend the architecture of a private cloud advanced analytics offering to serve the global needs of ING. Georgios has 10 years of professional experience in the design, architecture, and development of enterprise software and distributed systems. He has always believed in the incredible value hidden in data, which can be exploited through the use of today’s big data technologies and machine learning techniques. This fascination has guided him throughout his career to build multitransactional and highly scalable backend systems and data products in a variety of sectors, including the space and telecommunications industry.