PODCAST: Rise of MLOps in the Federal IT Arena

Written By Guest User

In the following Makpar Fed Mission Success podcast interview, Peter Cofrancesco, Innovation Engineer at Makpar, discusses the rise of Machine Learning Operations, or MLOps, in the federal IT arena.

Machine Learning Operations (MLOps) is a set of practices that aims to deploy and maintain Machine Learning (ML) models in production reliably and efficiently.  MLOps essentially streamlines IT development processes by building off of DevOps approaches that are specific to the ML domain. 

According to the Deloitte 2021 State of AI in the Enterprise survey, MLOps helps government agencies to better achieve overall Artificial Intelligence (AI) goals, and be prepared for managing AI risks. At the Makpar Innovation Lab, the team is using MLOps to identify optimal configurations and patterns to make IT development faster and more effective for Makpar’s government customers.

Following are highlights from this podcast interview:

  • A summary of the Makpar Innovation Lab’s core DevOps principles (0:25)

  • Detailed insights into what MLOps is, and how it streamlines IT development. (2:37)

  • How MLOps is benefiting Makpar’s government agency customers. (5:27)

The Makpar Innovation Lab is pioneering new ways for helping government to leverage neural networks for solving critical business problems with data. Learn more about the Makpar Innovation Llab here.

In addition, if you would like to learn more about Makpar’s MLOps capabilities, please contact us here.

 

Guest User
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