arrow_back Introductory workshop on Computational Machine Learning
Serverless Machine Learning Platform
Submitted by Vidyasagar Nallapati (@dumbyoda) on Monday, 30 April 2018
Technical level: Intermediate
A practical, architectural talk on a scalable, platform for machine learning as a service built on the principles of serverless architecture
Creating machine learning models, tuning, training, and scoring is a highly complex and long process. It requires a right combination of software, hardware, engineers, data scientists. The “As-a-Service”-based models and serverless architecture would fundamentally transform enterprise would solve business problems using machine learning and can change the cognitive application delivery space. This talk is about design patterns, architecture and implementation of a platform build on the principle of serverless architectures and scaling machine learning models as microservices. The core idea is to help developers and data scientists to focus on solving business problems using data using cutting-edge models, that spending time in operationalization. In this talk, I would be discussing overall platform, architecture and case studies on this
- Serverless Architecture and Machine Learning
- Machine Learning function as a service
- A Devops powered platform for creating, running and scaling machine learning functions
- Applications and Case Studies
- Refrences and Future
Vidyasagar N is a senior architect at Dell Technologies as part of IOT, Big Data and Advanced Analytics team from Bangalore. He loves all the things mathematics, statistics, machine learning, developer tools, automation and anything that will make systems better and easier, so he has found that in architecting, building and running large scale distributed services. He has a good practical experience in building rock-solid data pipelines prior to EMC in several startups, he has done his B.Tech from IIT BHU and has studied analytics at IIM Bangalore.
When Vidyasagar is not writing code, he can be found traveling, playing music and indulging in coffee.