The extraordinary benefits of AI in today’s business climate are abundantly clear. But as more machine learning solutions are implemented in organizations, the need for a centralized MLOps platform becomes even more significant. However, scaling such a solution across all lines of business has its challenges, with the production gap and cost of additional resources being two of the most influential ones.
The production gap means building a model but lacking the ability to deploy and manage it successfully in production. There are a lot of factors that can contribute to this gap. For example, even within one company, different lines of business often use different technology platforms and programming languages to solve their own unique issues, making it hard for IT to manage production models in a centralized way.
Cost of additional resources is the other major challenge. Many off-the-shelf tools designed to support production machine learning are simply cost prohibitive. They either require additional expenditure on unwanted infrastructure, or they drive up time and resources cost to manually code around gaps in their functionality.
DataRobot MLOps Agents Are the Solution.
MLOps Agents are designed to support any model, written in any language, deployed in any environment. This means that with minimal time or performance overhead, you can centrally monitor models created in any location and deployed on any infrastructure. This provides organizations with the flexibility required to meet model monitoring business needs by quickly providing health metrics for any deployed model.
There are three distinct layers to DataRobot’s remote model monitoring architecture: The MLOps Library, the Channel, and the Agent.
The MLOps Library tracks service, drift, and accuracy metrics. It provides near real-time, scalable monitoring to a highly scalable channel, such as Amazon SQS. It can also be used to report metrics outside of the prediction path to avoid any unwanted impacts.
Using a Channel, versus doing it directly, is the preferred way to pass metrics. DataRobot’s Agents provide multiple channels to provide more configuration options, including support for disconnected models that do not have network connectivity to the MLOps application.
The Agent is the third and final layer within the model architecture. It monitors and collects metrics from the channel and passes them back to the MLOps application to drive analytics on model service health, accuracy and data drift.
MLOps Agents provide centralized monitoring for all your production models, and they are undoubtedly the best solution for overcoming most significant production AI challenges.
To learn how to vastly improve your machine learning pipeline with our Agents, read our ebook, DataRobot MLOps Agents.
This ebook takes a deeper dive into the topics mentioned above and shows how to integrate MLOps Agents with your models. We’ll also show you how to configure and start the software.
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