MLOps for IT Teams Backgroun 1

MLOps for IT Teams

How to Transform the Machine Learning Lifecycle

IT, infrastructure, and data engineering teams are increasingly focusing on AI/ML initiatives as a means to drive top-line revenue and control bottom-line costs. In order to stay as competitive as possible, companies are backing this agenda with practical investments.

This accelerated pace of investment brings a new set of challenges. IT needs a way to meet the high bar around governance and compliance, while technology leaders need effective tools to meet these requirements. As organizations taking a manual route to production ML typically encounter issues with governance, automation might be the key to resolving these bottlenecks. Their efforts, however, might be misaligned with IT needs and capabilities and they may fail to get the desired business results.

In our ebook, MLOps for IT Teams, you’ll see some of the challenges IT, infrastructure, and data engineering teams face when trying to scale ML efforts, as well as some of the ways robust machine learning operations (MLOps) solutions can help deliver the promise of AI.

Download this ebook to learn about:

  • The current state of AI and how the latest trends influence IT and infrastructure teams
  • Some of the key developments in ML lifecycle automation
  • The importance of a robust machine learning operations infrastructure
  • DataRobot MLOps and its transformative effect on the machine learning life cycle
Organizations need to run their models close to the data and applications that need them most—and increasingly, this means running those models across geographic and regulatory boundaries. This underscores the need for an end-to-end AI platform with MLOps that supports distributed serving.
Kenny Daniel
Kenny Daniel

CTO of MLOps, DataRobot

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