Best Practices for Operationalizing Data Science & Machine Learning

Scaling AI Lynn Heidmann

Roger Magoulas, VP of Radar at O'Reilly Media, Inc., asks: Why is the final mile such a challenge for so many organizations who are working on AI and machine learning? Dataiku Data Scientist Jed Dougherty has answers.

What it Takes

In their interview at Strata Data Conference 2019, Jed discusses the fact that being able to go from sandbox to deployed machine learning model in a production environment requires:

  1. Good data governance, including the ability to monitor and track the quality of machine learning models over time.

  2. Solid tools to bridge the gap and allow data scientists to work in a framework while actually building a model (and that are usable and don't reduce the pool of people that can actually operationalize).

Watch the Interview

Catch the 7-minute interview from Strata Data Conference 2019 in New York City:

Go Further:
Get the Guidebook on Data Science Operationalization

Operationalizing data science projects it is not an easy task — it becomes twice as hard when teams are isolated and playing by their own rules. Get the guidebook that shows how to break down silos and make operationalization a company-wide standard for data science.

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