Building Tools for Enterprise Data Science
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In this episode of the Data Show, I spoke with Vitaly Gordon, VP of data science and engineering at Salesforce. As the use of machine learning becomes more widespread, we need tools that will allow data scientists to scale so they can tackle many more problems and help many more people. We need automation tools for the many stages involved in data science, including data preparation, feature engineering, model selection and hyperparameter tuning, as well as monitoring. I wanted the perspective of someone who is already faced with having to support many models in production. The proliferation of models is still a theoretical consideration for many data science teams, but Gordon and his colleagues at Salesforce already support hundreds of thousands of customers who need custom models built on custom data. They recently took their learnings public and open sourced TransmogrifAI, a library for automated machine learning for structured data, which sits on top of Apache Spark.
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