Declarative Machine Learning Without The Operational Overhead Using Continual

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 When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Schema changes, missing data, and volume anomalies caused by your data sources can happen without any advanced notice if you lack visibility into your data-in-motion. That leaves DataOps reactive to data quality issues and can make your consumers lose confidence in your data. By connecting to your pipeline orchestrator like Apache Airflow and centralizing your end-to-end metadata, Databand.ai lets you identify data quality issues and their root causes from a single dashboard. With Databand.ai, you’ll know whether the data moving from your sources to your warehouse will be available, accurate, and usable when it arrives. Go to dataengineeringpodcast.com/databand to sign up for a free 30-day trial of Databand.ai and take control of your data quality today. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription 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 LinkedIn @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. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Continual World Bank SAS SPSS Stata Feature Store DataRobot Transfer Learning dbt Podcast Episode Ludwig Overton (Apple) Hightouch Census Galaxy Schema In-Database ML Podcast Episode scikit-learn Snorkel Podcast Episode Materialize Podcast Episode Flink SQL The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast