One of the biggest problems with creating ML models is that the models are built in environments that are useless for deployment.

The fundamental issue is that Machine Learning deployment is a young and immature field, and it hasn’t yet developed the toolkits that database or software development have. Databases, for example, are widely available, stable, (sometimes) scalable and extremely fast. Because of this, we’re going to piggyback on the work that database engineers have done, and use their tools to our advantage. Here, we’ll focus on using scale-out RDBMS for model deployment.

#database #machine-learing #simple #data