Whitepaper

Generate Business Value with AI Using the Production-First Approach to MLOps

How do I serve models for real-time enterprise applications?

You are basically asking for model serving or a way to manage and deliver your models in a secure and governed way to production.

There are a few things you need to think about:

  1. How will my models be managed?
  2. How will my models be delivered (served) for inferencing?
  3. Do I need real-time or batch level delivery?

In its simplest form, you store or deploy the trained model to a remote repository known as a model server. Then at runtime, you retrieve the model, pass features (inputs) into it and predict.
There's a lot of value in this simple model. Firstly, your models are stored in a central repository which provides governance, share-ability, versioning and reusability. It should be as easy as a few function calls.
Secondly, retrieving the model should also be as easy as a single function call. However, you must ensure the appropriate protocols are supported and are secure.


A great way to accomplish this is using  MLRun Serving Pipelines.  Using MLRun Serving uses the Nuclio real-time serverless framework for the pipelines.

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