Model Server: The Critical Building Block of MLOps

When we think of?machine learning, what comes to mind are the datasets, algorithms, deep learning frameworks, and training the neural networks. While they play an important role in the lifecycle of a model, there is more to it. The most crucial step in a typical machine learning operations (MLOps) implementation is deploying and monitoring models, which is often an afterthought.

A common misconception is that deploying models is as simple as wrapping them in a Flask or Django API layer and exposing them through a REST endpoint. Unfortunately, this is not the most scalable or efficient approach in operationalizing ML models. We need a robust infrastructure for managing the deployments and the inference of the models.

Read the entire article at?The New Stack

Janakiram MSV?is an analyst, advisor, and architect. Follow him on?Twitter,??Facebook?and?LinkedIn.