Friday 12:00–12:30 in Track 1

From a model to production like a Pro: Software-engineering Best-Practices

Marcel KrĨah

Audience level:
Intermediate

Description

After an initial data-science proof-of-concept is completed, it often needs to be professionalized and deployed to production. Underestimating this step often leads to higher maintenance and slower time-to-market of new features. In this talk we will cover a set of practical software-engineering best-practices for industrializing a machine-learning model to help minimize such problems.

Abstract

After an initial data-science proof-of-concept is completed, it often needs to be professionalized and deployed to production. Underestimating this step often leads to higher maintenance and slower time-to-market of new features. In this talk we will cover a set of practical software-engineering best-practices for industrializing a machine-learning model to help minimize such problems.

After this talk, you will know how to develop and run a professional data-science application in production like a pro. Among others, we will cover topics like:

The talk is based on my 10-year experience as a software-engineer and all the stuff I have learnt about developing and deploying software applications during this time.

This talk is suitable for everyone responsible for running a data-science model in production environments.

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