Autonomous

OML Notebooks Interpreter Bindings

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When using Oracle Machine Learning notebooks, you can export and import these between different projects and different environments (from ADW to ATP).

But something to watch out for when you import a notebook into your ADW or ATP environment is to reset the Interpreter Bindings.

When you create a new OML Notebook and build it up, the various Interpreter Bindings are automatically set or turned on. But for Imported OML Notebooks they are not turned on.

I’m assuming this will be fixed at some future point.

If you import an OML Notebook and turn on the Interpreter Bindings you may find the code in your notebook cells running very slowly

To turn on these binding, click on the options icon as indicated by the red box in the following image.

Screenshot 2019-08-19 21.04.58

You will get something like the following being displayed. None of the bindings are highlighted.

Screenshot 2019-08-19 21.08.03

To enable the Interpreter Bindings just click on each of these boxes. When you do this each one will be highlighted and will turn a blue color.

Screenshot 2019-08-19 21.07.20

All done!  You can now run your OML Notebooks without any problems or delays.

 

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Oracle Machine Learning notebooks

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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:

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