The 4 Pillars of MLOps, Part 3: Model Lifecycle Management

Podcast
DataRobot Podcast resource card part 3 v.1.0 1

You want to scale your use of AI, but you are blocked by production issues. MLOps will get your AI projects out of the lab and into production where they can generate value and help transform your business. With MLOps, your Data Science and IT Operations teams can collaborate to deploy and manage models in production.

MLOps also recognizes that models need to be updated frequently and seamlessly. In this 15-minute podcast produced by Data Science Central, you’ll discover how model lifecycle management supports the testing and warm-up of replacement models, A/B testing of new models against older versions, seamless rollout of updates, failover procedures, and full version control for simple rollback to prior model versions.

DataRobot Podcast resource card part 3 v.1.0 1
  • DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely – it's almost like magic!
    Omair Tariq

    Data Analyst, Symphony Post Acute Network

  • I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
    Oliver Rees

    General Manager – Torque Data at Virgin Australia

  • At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
    Akshay Tandon
    Akshay Tandon

    VP of Strategy Analytics, LendingTree

  • DataRobot allows us to understand the data that’s being fed into our models without blindly feeding whatever we get into our system. DataRobot makes my team very effective.
    Deena Narayanaswamy
    Deena Narayanaswamy

    Head of Data Insights, Avant

  • We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.
    Evariant