The 4 Pillars of MLOps, Part 2: Production Model Monitoring

Podcast
DataRobot Podcast resource card part 2 v.1.0 1

You want to scale your use of AI, but you are blocked by production issues. Which means that data scientists have to help deploy and maintain their models, which is costly and takes away from doing new data science.

What if there was a better way? Machine Learning Operations (MLOps) will get your AI projects out of the lab and into production where they can generate value and help transform your business.

In this 15-minute podcast produced by Data Science Central, we explore best practices in Production Model Monitoring. With MLOps, your monitoring is designed for machine learning. Monitoring includes service health, data drift, model accuracy, and proactive alerts that are sent to stakeholders using a variety of channels like email, Slack and Pagerduty, based on severity. With MLOps monitoring in place, your teams can deploy and manage thousands of models, and your business will be ready to scale production AI.

DataRobot Podcast resource card part 2 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