Data Engineering Podcast
© 2021 Tobias Macey
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Declarative Machine Learning Without The Operational Overhead Using Continual
An interview with Tristan Zajonc about his work at Continual to make declarative machine learning workflows possible and seamless by building on top of the data warehouse, and how it reduces the time and cost of putting machine learning into production.
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.
: 19-9-2021 23:13:03
This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.
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