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Make Data Work
Dec 4–5, 2017: Training
Dec 5–7, 2017: Tutorials & Conference

Engineering cloud-native machine learning applications

11:15am11:55am Wednesday, December 6, 2017
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Who is this presentation for?

  • Software developers, data scientists, data engineers, and product managers

What you'll learn

  • Learn how to design and build cloud-native machine learning applications


In the current Agile business environment, where developers are required to experiment multiple ideas and also react to various situations, doing cloud-native development is the way to go. However, being cloud native means autoprovisioning, autoscaling, and autoredundancy. The benefits are huge, but writing cloud-native applications requires a major shift in the developer mindset from modules to services and from object-oriented to functional code.

Harjinder Mistry and Bargava Subramanian explain how to design and build a microservices-based cloud-native machine learning application, drawing on their own application, written and deployed entirely in Python. The application consists of multiple microservices that collaborate with each other to ingest data and generate real-time machine learning-driven recommendations for end users and customers. The microservices are deployed on OpenShift; the data is stored in cloud storage; and the application leverages Amazon Web Services and Google Cloud Platform for cloud compute and machine learning. You’ll learn to visualize the different layers involved in cloud computing and leave able to write your own cloud-native applications with ease.

Topics include:

  • How to divide the application into a set of microservices
  • How to debug and unit test program
  • How to set up the required environment in the cloud
  • How to write program in a cluster-native way
  • How to handle different data sources
  • How to write deployment scripts
Photo of Harjindersingh Mistry

Harjindersingh Mistry


Harjinder Mistry is a principal research engineer at Ola, where he is building a cloud-native data-science platform to solve challenging problems of fleet management. Previously, he engineered data platforms for a couple of interesting data-science projects: at Red Hat and the Watson ML platform at IBM. Earlier, he spent several years in the DB2 SQL Query Optimizer team, building and fixing the mathematical model that decides the query execution plan. Harjinder holds an MTech from IIIT, Bangalore, India.

Photo of Bargava Subramanian

Bargava Subramanian


Bargava Subramanian is a cofounder and deep learning engineer at Binaize in Bangalore, India. He has 15 years’ experience delivering business analytics and machine learning solutions to B2B companies. He mentors organizations in their data science journey. He holds a master’s degree from the University of Maryland, College Park. He’s an ardent NBA fan.