Building tools for enterprise data science

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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Here are some highlights from our conversation:

The need for an internal data science platform

It’s more about how much commonality there is between every single data science use case—how many of the problems are redundant and repeatable.

… A lot of data scientists solve problems that honestly have a lot to do with engineering, a lot to do with things that are not pure modeling.

TransmogrifAI

TransmogrifAI is an automated machine library for mostly structured data, and the problem that it aims to solve is that we at Salesforce have hundreds of thousands of customers. While all of them share a common set of data, the Salesforce platform itself is extremely customizable. Actually, 80% of the data inside the Salesforce platform actually sits in what we refer to as custom objects, which one can think of as custom tables in a database.

… We don’t build models that are shared between customers. We always use a single customer’s data. We have hundreds of thousands of models potentially that we need to build, and because of that, we needed to automate the entire process. We just cannot throw people at the problem. We basically created TransmogrifAI to automate the entire end-to-end process for creating a model for a user and we decided to open source it a couple months ago.

Related resources:

• “What machine learning means for software development”
• “We need to build machine learning tools to augment machine learning engineers”
• Francesca Lazzeri and Jaya Mathew on “Lessons learned while helping enterprises adopt machine learning”
• Tim Kraska on “How machine learning will accelerate data management systems”
• “Managing risk in machine learning models”: Andrew Burt and Steven Touw on how companies can manage models they cannot fully explain.
• “Lessons learned turning machine learning models into real products and services”