Declarative Machine Learning Without The Operational Overhead Using Continual
Data Engineering Podcast
The Model Life Cycle Aspects
In terms of the sort of model life cycle aspects. Obviously you're managing the re training and understanding when there's model drift happening. But for teams that want to have more visibility into what the model is doing, or wants to be able to dig into some of the explainability aspects of the model. What are some of the options that they have for being able to interact with the model as it's run?
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
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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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 someone building a model and putting it into production?
- Once a model has been deployed, what are the mechanisms that you expose for interacting with it?
- How does this differ from in-database ML capabilities such as what is offered by Vertica and BigQuery?
- How much understanding of ML/AI principles is necessary for someone to create a model with Continual?
- What is your estimation of the impact that Continual can have on the overall productivity of a data team/data scientist?
- What are the most interesting, innovative, or unexpected ways that you have seen Continual used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Continual?
- When is Continual the wrong choice?
- What do you have planned for the future of Continual?
Contact Info
- @tristanzajonc on Twitter
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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Links
- Continual
- World Bank
- SAS
- SPSS
- Stata
- Feature Store
- DataRobot
- Transfer Learning
- dbt
- Ludwig
- Overton (Apple)
- Hightouch
- Census
- Galaxy Schema
- In-Database ML Podcast Episode
- scikit-learn
- Snorkel
- Materialize
- Flink SQL
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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