Explore Model Governance Using Our Mlflow Agent

In today's AI-driven era, maintaining an organized MLOps framework is essential. XMPro's MLflow Agent is your go-to solution for streamlined model governance, ensuring you never lose track of model versions or their locations.

What's Inside:

Need for Governance: Emphasizing the corporate need to organize AI within an MLOps framework. XMPro's MLflow Agent: Introducing a game-changing tool for effective model governance using a well known MLOps toolkit. Flexible Integration: Easily integrate with diverse repositories. Live Demo: Witness how data scientists can update model versions in MLflow without altering the data stream.

Seamless Transition: A practical showcase of switching between model versions, demonstrating the tool's agility.

Key Takeaway: Stay ahead in the AI game by efficiently governing your models. With XMPro's MLflow Agent, transition seamlessly between model versions without hassle.

If you found this video valuable, please Subscribe and tap the Bell Icon for more updates!

#MLOps #ModelGovernance #XMPro #MLflowAgent #AIIntegration #ModelVersioning #DataScience #AIScaling #ModelManagement #WineQualityModel #AIFrameworks #MachineLearning #PredictionModels #DataStream #AIInnovation #SeamlessTransition #TechDemo #AIOrganisation #MLTools #MLOpsTools

Transcript

as AI scales within the organization

corporate guard rails require AI to be

modeled within an mlops framework you

don't want to end up with models stored

in a variety of places and lose track of

which is the latest version or where it

is

located ml flow agent is the first in a

series that enables effective model

governance using a popular mlops tool

set let us know if you are using a

different

repository this empowers data scientists

to promote new model versions within MLF

flow without going back to edit the data

stream let's see this in

action my thanks to Chris for recording

this

demo as soon as I find my

mouse in it we have an mlflow agent

configured to use version one

of a model called wine

quality once the data stream is

published observe that the first event

confirms model version one was used to

make a

prediction now we'll change over to

mlflow and promote version two to

production

going back to the data stream without

reconfiguring or republishing observe

that model version 2 is seamlessly used

to make the next

prediction

Last updated