Oracle says the latest release of its cloud-based Autonomous Data Warehouse will help transform complex data transformation activities into point-and-click tasks. The Oracle Autonomous Data Warehouse is a software-as-a-service cloud data warehouse that Oracle says is designed to reduce the complexities of developing data driven applications by automating critical provisioning and configuration tasks. With this updated release, the company hopes to make the data warehouse service easier to operate for both professionals and non-IT users. As per reports, the most interesting feature of the new release is AutoML, which will automate several time intensive tasks and instead help users create machine learning (ML) models using a no-code interface. Business users will be drag-and-drop data sets that AutoML will then run through different ML algorithms in order to produce meaningful information that can be easily interpreted and digested by business users.

This post is about Machine Learning with SQL. It makes sense to build/run Machine Learning models where data stays -- in the database. Step by step info on how to get started. Python (and soon JavaScript with TensorFlow.js) is a dominant language for Machine Learning. There is a way to build/run Machine Learning models in SQL.

Python (and soon JavaScript with TensorFlow.js) is a dominant language for Machine Learning. There is a way to build/run Machine Learning models in SQL. There could be a benefit to run model training close to the database, where data stays. With SQL we can leverage strong data analysis out of the box and run algorithms without fetching data to the outside world (which could be an expensive operation in terms of performance, especially with large datasets). This post is to describe how to do Machine Learning in the database with SQL.

We use data from a popular Kaggle competition, the Wisconsin Breast Cancer data, to build a binary classification model for the liklihood of a tumor being benign or malignant. We see how OAC's Data Visualization can be used to profile & explore the data, and can be used to do a rapid prototype of a Machine Learning model with DVML. See how ADW can be used to easily drop a Machine Learning model into production and enabled as a REST API for custom Applications and websites. By registering for this TechCast you give permission for your name and email address to be shared with the presenter and for BIWA User Community so we can inform you of future TechCasts and conferences of interest.

One of the relatively new features available with Oracle Autonomous Data Warehouse is Oracle Machine Learning Notebook. The description on Oracle's tutorial site states: "An Oracle Machine Learning notebook is a web-based interface for data analysis, data discovery, and data visualization." If you are familiar with Jupyter Notebooks (often Python based) then you may know and appreciate the Wiki like combination or markdown text and code snippets that are ideal for data lab'explorations' of data sets and machine learning models. I am quite a fan myself. Especially wrangling data, juggling with Pandas Data Frames and visualizing data with Plotly is good fun and it is quite easy to accomplish meaningful and advanced results.