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.
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: OpenShift.io 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.
Bargava Subramanian is a machine learning engineer based in Bangalore, India. Bargava has 14 years’ experience delivering business analytics solutions to investment banks, entertainment studios, and high-tech companies. He has given talks and conducted numerous workshops on data science, machine learning, deep learning, and optimization in Python and R around the world. He mentors early-stage startups in their data science journey. Bargava holds a master’s degree in statistics from the University of Maryland at College Park. He is an ardent NBA fan.
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