ML Models — for everyone & everywhere!

Published in

Analytics Vidhya

2 min readApr 22, 2020

We have come across data science platforms and ML offerings targeted for expert audiences who have Python/ R/ Matlab..etc skills and who understand algorithms/ kernels..etc.

But, what if people who understand data very well but do not have expert skills also need to explore the AI/ ML world?

5 ways to build ML models in the Oracle World are

Oracle Cloud Infrastructure Data Flow — https://www.oracle.com/big-data/data-flow/
Oracle Analytics Cloud (OAC) — https://www.oracle.com/au/business-analytics/analytics-cloud.html
Oracle Machine Learning (OML)-https://www.oracle.com/database/technologies/datawarehouse-bigdata/machine-learning.html
Oracle Autonomous Database — https://www.oracle.com/au/database/autonomous-database.html
Oracle Cloud Infrastructure Data Science — https://www.oracle.com/data-science/cloud-infrastructure-data-science-product.html

In this article, we see how to build a binary classification model in 3 different ways using the same data source. Based on the interest (medium claps) I will then extend this article to include the other 2 ways

Data Source — Oracle Autonomous Database

Binary Classification ML Model using:

Using SQL/ PLSQL in the Oracle Database (Build in the database and execute in the database — move the code and not the data)
Using Oracle Analytics Cloud (Build where data is being analyzed and visualized — BI/ Viz)
Using Oracle Cloud Infrastructure Data Science (Build in a dedicated Data Science Environment)

Using Apache Zeppelin notebooks using SQL/ PLSQL in the Oracle Autonomous Database

Using Oracle Analytics Cloud

Using Oracle Cloud Infrastructure Data Science

I have covered this in the below article