Laying an MLOps Foundation A Fireside Chat with Featured Speaker Mike Gualtieri hero banner
On-Demand Webinar

Laying an MLOps Foundation: A Fireside Chat with Featured Speaker Mike Gualtieri

While you have hired top-notch data scientists to build models and invested in data science tools, you still can’t get your AI projects off the ground. Research shows that the share of AI models created but never put into production is anywhere between 50 and 90%. So what can teams do to gain value from AI?

The practice of Machine Learning Operations (MLOps) or ModelOps solves this challenge by bridging the gap between data teams and IT Ops teams, providing the capabilities that both teams need to work together to deploy, monitor, manage, and govern machine learning models in production. In this fireside chat, Featured Speaker Mike Gualtieri, VP, Principal Analyst at Forrester, will provide insight into what MLOps is and how any organization can use it to finally derive value from machine learning projects.

Attendees will learn:

  • Best practices required for successful and sustained deployment of machine learning in production;
  • The importance of managing the dynamic nature of machine learning applications; and
  • Why a strong MLOps strategy is required to enforce governance policies.
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    Omair Tariq
    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
    Oliver Rees

    General Manager – Torque Data at Virgin Australia

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    Akshay Tandon
    Akshay Tandon

    VP of Strategy Analytics, LendingTree

  • 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.