Summary
Building, scaling, and maintaining the operational components of a machine learning workflow are all hard problems. Add the work of creating the model itself, and it’s not surprising that a majority of companies that could greatly benefit from machine learning have yet to either put it into production or see the value. Tristan Zajonc recognized the complexity that acts as a barrier to adoption and created the Continual platform in response. In this episode he shares his perspective on the benefits of declarative machine learning workflows as a means of accelerating adoption in businesses that don’t have the time, money, or ambition to build everything from scratch. He also discusses the technical underpinnings of what he is building and how using the data warehouse as a shared resource drastically shortens the time required to see value. This is a fascinating episode and Tristan’s work at Continual is likely to be the catalyst for a new stage in the machine learning community.
Announcements
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Your host is Tobias Macey and today I’m interviewing Tristan Zajonc about Continual, a platform for automating the creation and application of operational AI on top of your data warehouse
Interview
Introduction
How did you get involved in the area of data management?
Can you describe what Continual is and the story behind it?
What is your definition for "operational AI" and how does it differ from other applications of ML/AI?
What are some example use cases for AI in an operational capacity?
What are the barriers to adoption for organizations that want to take advantage of predictive analytics?
Who are the target users of Continual?
Can you describe how the Continual platform is implemented?
How has the design and infrastructure changed or evolved since you first began working on it?
What is the workflow for
