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How to Utilize Python Machine Learning Models

Ever trained a new model and just wanted to use it through an API straight away? Sometimes you don't want to bother writing Flask code or containerizing your model and running it in Docker. If that sounds like you, you definitely want to check out MLServer. It's a Python-based inference server that recently went GA, and what's really neat about it is that it's a highly-performant server designed for production environments. That means that, by serving models locally, you are running in the exact same environment as they will be in when they get to production.

This blog walks you through how to use MLServer by using a couple of image models as examples.

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How to Tackle Challenges Deploying ML Models

Highlights

  • By deploying machine learning models, other teams in your company can use them, send data to them, and get their predictions, which are in turn populated back into the company systems to increase training data quality and quantity. 
  • Once this process is initiated, companies will start building and deploying higher numbers of machine learning models in production. They will master robust and repeatable ways to move models from development environments into business operations systems.

Today's data scientists and developers have a much easier experience when building AI-based solutions through the availability and accessibility of data and open source machine learning frameworks. This process becomes a lot more complex, however, when they need to think about model deployment and pick the best strategy to scale up to a production-grade system. 

In this article, we will introduce some common challenges of machine learning model deployment. We will also discuss the following points that may enable you to tackle some of those challenges: