In this article, we're going to walk you through building and deploying a Machine Learning model into a database using familiar tools.
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.
Your Data Architecture: Simple Best Practices for Your Data Strategy. Don't miss this helpful article.
A data lake is totally different from a data warehouse in terms of structure and function. Here is a truly quick explanation of "Data Lake vs Data Warehouse".
SingleStore: The One Stop Shop For Everything Data. SingleStore offers a unified database to facilitate fast analytics for organisations looking to embrace diverse data and accelerate their innovations.
How To Create A Simple Excel Data Set From the SEER Database. An application of the SEER*Stat software...
In this post, we'll learn Getting Started With Data Lakes.<br><br> This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that's designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You'll also explore key benefits and common use cases.