Isn’t it ironic that AI has not, so far, had much impact on the development workflow of software developers and AI/ML engineers? In fact, software development has become so complex that smart intelligent tools have become a necessity rather than something good to have. There has been a paradigm shift toward full stack developers—generalists rather than specialists. As more and more companies go Agile, developers and engineers are expected to do rapid application development.
One of the challenges faced by developers is to choose the right combination of software technologies and components from a wide pool. This problem is amplified by two challenges: the developer might have little to no idea about the software stack she is supposed to choose and work with, and these technologies themselves are continuously evolving. It’s a daunting task to make these choices and succeed in a short span of time.
Bargava Subramanian and Harjinder Mistry explain how they built a set of smart tools to help developers. There’s a bot, there’s a customized developer-focused natural language system, and there’s a bunch of microservices in the cloud, which all help drive the real-time recommendation system for the developers. A variety of data sources (e.g., GitHub, Stack Overflow, user information, and technical discussions) were used to build the AI system. The entire system is cloud native and runs as microservices on OpenShift. Bargava and Harjinder explain the analytics microservice architecture thought process and how they leverage both AWS EMR clusters and Google Cloud Platform.
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, and 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.
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.
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