Last week I wrote a blog post about how long it took to create machine learning models on Oracle Database Cloud service. There was some impressive results and some surprising results too.
I decided to try out the exact same tests, using the exact same data on the Oracle Autonomous Data Warehouse Cloud service (ADW).
When creating the ADW service I took the basic configuration and didn’t change anything. The inbuilt machine learning for the Autonomous service will magically workout my needs and make the necessary adjustments, Right? It can handle any data volume and any data processing requirements, Right?
Here are the results.
* You will notice that there is no time given for creating a SVM model for the 10M record data set. After waiting for 4 hours I got bored and gave up waiting (I actually did this three time to make sure it wasn’t a once off)
[I also had a 50M record data set. I just didn’t waste time trying that.]
[Neural Networks algorithm hasn’t been ported onto ADW at this point in time]
If you look back at the results from using the DBaaS you will see it was significantly quicker than the ADW. (for some it would be quicker using Python on my laptop)
Before you believe the hype, go test it yourself and make sure it measures up.
I re-ran my test cases over a number of days to see if the machine learning aspect of the Autonomous kicked in to learn from the processing and make any performance improvements. Sadly the results were basically the same or slightly slower. Disappointing.
When some tells you, you should be using this, ask them have they actually used and tested it themselves. And more importantly, don’t believe them. Go test it yourself.
In this blog post I’ll have a look at Oracle Machine Learning notebooks, some of the example notebooks and then how to create a new one.
Check out my previous blog posts on ADWC.
On entering Oracle Machine Learning on your ADWC service, you will get the following.
Our starting point is to example what is listed in the Examples section. Click on the Examples link. The following lists the example notebooks.
Here we have examples that demonstrate how to build Anomaly Detection, Association Rules, Attribute Importance, Classification, Regression, Clustering and one that contains examples of various statistical function.
Click on one of these to see the notebook. The following is the notebook demoing the Statistical Functions. When you select a notebook it might take a few seconds to setup and open. There is some setup needed in the background and to make sure you have access to the demo data and then runs the notebook, generating the results. Most of the demo data is based on the SH schema.
Now let us create our first notebook.
From the screen shown above lift on the menu icon on the top left of the screen.
And then click on Notebooks from the pop-out menu.
In the Notebooks screen click on the Create button to create your first notebook.
And give it a meaningful name.
The Notebook shell will be created and then opened for you.
In the grey box, just under the name the name of your Notebook, is where you can enter your first SQL statement. Then over on the right hand side of this Cell you will see a triangle on its side. This is the run button.
For now you can only run SQL statements, but you also have other notebooks features such as different charting options and these are listed under the grey cell, where your SQL is located.
Here you can create Bar, Pie, Area, Line and Scatter charts. Here is an example of a Bar chart.
Warning: You do need to be careful of your syntax, as minimal details are given on what is wrong with your code. Not even the error numbers.
Go give it a good and see how far you can take these OML Notebooks.
With the recent release of Oracle’s Autonomous Data Warehouse Cloud (ADWC), Oracle has given data scientists a new tool for data discovery and machine learning on the ADWC. Oracle Machine Learning is based on Apache Zeppelin and gives us a new machine learning tool for accessing the in-database machine learning algorithms and in-database statistical functions.
Oracle Machine Learning (OML) SQL notebooks provide easy access to Oracle’s parallelized, scalable in-database implementations of a library of Oracle Advanced Analytics’ machine learning algorithms (classification, regression, anomaly detection, clustering, associations, attribute importance, feature extraction, times series, etc.), SQL, PL/SQL and Oracle’s statistical and analytical SQL functions. Oracle Machine Learning SQL notebooks and Oracle Advanced Analytics’ library of machine learning SQL functions combined with PL/SQL allow companies to automate their discovery of new insights, generate predictions and add “AI” to data viz dashboards and enterprise applications.
The key features of Oracle Machine Learning include:
- Collaborative SQL notebook UI for data scientists
- Packaged with Oracle Autonomous Data Warehouse Cloud
- Easy access to shared notebooks, templates, permissions, scheduler, etc.
- Access to 30+ parallel, scalable in-database implementations of machine learning algorithms
- SQL and PL/SQL scripting language supported
- Enables and Supports Deployments of Enterprise Machine Learning Methodologies in ADWC
Here is a list of key resources for Oracle Machine Learning:
- Oracle Machine Learning Notebooks
- Video overview of Oracle Machine Learning
- Download sample Oracle Machine Learning notebooks
- Quick Start Tutorial for getting started with Oracle Machine Learning
- Documentation: Using Oracle Machine Learning