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.
Have you ever woken up to a crisis because a number on a dashboard is broken and no one knows why? Or sent out frustrating slack messages trying to find the right data set? Or tried to understand what a column name means?
Our friends at Atlan started out as a data team themselves and faced all this collaboration chaos themselves, and started building Atlan as an internal tool for themselves. 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.
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Your data platform needs to be scalable, fault tolerant, and performant, which means that you need the same from your cloud provider. Linode has been powering production systems for over 17 years, and now they’ve launched a fully managed Kubernetes platform. With the combined power of the Kubernetes engine for flexible and scalable deployments, and features like dedicated CPU instances, GPU instances, and object storage you’ve got everything you need to build a bulletproof data pipeline. If you go to dataengineeringpodcast.com/linode today you’ll even get a $100 credit to use on building your own cluster, or object storage, or reliable backups, or… And while you’re there don’t forget to thank them for being a long-time supporter of the Data Engineering Podcast!
Databand.ai is a unified Data Observability Platform that helps DataOps teams catch and solve data health issues fast. Databand.ai’s platform helps data engineers pinpoint pipeline issues and quickly identify their root cause so DataOps can begin working on a resolution before bad data is delivered. Whether you’re using Apache Spark, Apache Airflow, Databricks, Amazon S3, self-hosted python scripts, or combinations of these, Databand.ai allows you to monitor data health along every step of its journey. Powerful integrations to 20+ tools gives you full visibility of your stack. Our mission is to help businesses trust their data with the most powerful Data Observability Platform. Experience unified observability with a free trial today: www.databand.ai
- 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
- 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?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- 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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