This series demonstrates how data engineers and data scientist can predict the price of a house based on historical data. Using code patterns with sample code, you’ll learn about built-in stored procedures, building machine learning models, and using IBM Db2 Warehouse on Cloud to create a web application using Node.js to create, update, and delete records from the database.
- Blog Post
A developers guide to data for AI
IBM Db2 Warehouse has built-in machine learning functions to train and run models on data residing in Db2 Warehouse on Cloud. Operations are performed by the Db2 Warehouse engine itself without having to move the data. This way you can achieve greater performance in terms of computations and retrieval of…
- Code Pattern
Collect home sales data using a high performance CRUD app
This code pattern uses IBM Db2 Warehouse on Cloud to create a web application using Node.js to create, update and delete records from the database. It also uses Angular to build a front-end UI to display, save and update data, to and from, the Db2 database. This code pattern shows…
- Code Pattern
Predict home value using Golang and in-memory database machine learning functions
This pattern will show you how to create an AI application written in Golang--using the IBM Db2 Warehouse on Cloud built-in stored procedures to train and run models on data residing in IBM Db2 Warehouse on Cloud. This specific application runs the built-in linear regression stored procedure to predict home…
- Code Pattern
Predict home value using Python and machine learning
This code pattern demonstrates a data scientist's journey in creating a machine learning model using IBM Watson Studio and IBM Db2 on Cloud. The pattern uses Jupyter notebook to connect to the Db2 database and uses a machine learning algorithm to create a model which is then deployed to IBM…