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: