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.
Richa Khandelwal is a software engineering manager at Nike. An engineer with over eight years of experience in the software industry, Richa has worked in the automotive, financial, and retail spaces. Her expertise lies in backend systems, big data, and machine learning. In her free time, she loves traveling and has a personal goal of seeing at least one new country every year.
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