DevOps for Data Science: Lifecycle of Big Data analytics services

Title:

DevOps for Data Science: Building, testing, releasing, monitoring and maintaining big data analytics services.

Abstract: “The next breakthrough in data analysis may not be in individual algorithms, but in the ability to rapidly combine, deploy, and maintain existing algorithms.” (c) Hazy: Making it Easier to Build and Maintain Big-data Analytics. Provisioning a Hadoop/Spark cluster is not a big deal anymore. But how do you go beyond the basic proof of concept to collecting and preparing data, building machine learning model and analytics report? As a data scientists and engineers, we should realize that there are a lot of challenges to solve after PoC iteration before we can start to see big data deliver sustainable value and decision support to businesses. In this talk we’ll discuss how to move data exploration and research projects into operations and continuously monitor, update and add new features reliably. We’ll cover cultural and technical challenges to establishing devOps process for big data analytics teams then continue with real life use cases, code samples and a live demo.

Stepan Pushkarev is CTO and co-founder of Hydrosphere.io. He led engineering teams for eCommerce, IoT and Ad-tech companies. Being responsible for the full products stack from math models, infrastructure & operations, to the web & mobile applications as well as for hiring and establishing engineering culture and delivery process he combines strong technology, management end entrepreneur backgrounds.

AGENDA

6 pm -- Door opens

6 -- 6 : 30 pm check-in and networking

7 pm --- Yelp door closed. No more entry.

6:30 pm -- 7:30 pm main talk + QA

7:30 pm -- 8 :00 pm QA + Closing

8:30 office is closed