December 31, 2019

161 words 1 min read

Data science in production

Data science in production

Richa Khandelwal explores where engineering fits in machine learning land and shares software engineering and DevOps practices that help in taking a machine learning-powered end-user experience from inception to production.
Talk Title Data science in production
Speakers Richa Khandelwal (Nike)
Conference O’Reilly Open Source Convention
Conf Tag Put open source to work
Location Portland, Oregon
Date July 16-19, 2018
URL Talk Page
Slides Talk Slides
Video

Creating machine learning models consists of many pieces: data cleaning, analysis, statistics, training, accuracy analysis, etc. Developing a model is still just the tip of the iceberg when it comes to delivering a machine learning or deep learning solution in production. Richa Khandelwal explores where engineering fits into data science and shares software engineering and DevOps practices that help in taking a machine learning model to production. Along the way, Richa explains how to enforce quality without complete code rewrites and how blurring the lines between data scientists and engineers helps deliver solutions more quickly.

code data science deep learning machine learning devops
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