2 Get Started

This section discusses how to get started with Oracle Machine Learning User Interface, and use Apache Zeppelin-based machine learning notebooks along with the OML Notebooks, where you can perform data exploration and data visualization, data preparation and machine learning.

  • Get Started with Oracle Machine Learning User Interface
    Here is how you can get started with Oracle Machine Learning User Interface (UI).
  • Access Oracle Machine Learning User Interface
    You can access Oracle Machine Learning User Interface from Autonomous AI Database.
  • Oracle Machine Learning User Interface Home Page
    The Oracle Machine Learning User Interface home page provides you quick links to important interfaces, help links, and the log of your high-level recent activities.
  • Oracle Machine Learning Notebooks
    Oracle Machine Learning Notebooks is a collaborative web-based interface that supports notebook creation, scheduled run, and versioning. You can document work using Markdown and run SQL, R, and Python code for data exploration, visualization, and preparation, and machine learning model building, evaluation, and deployment. Oracle Machine Learning Notebooks also provide the Conda interpreter to create a custom conda environment that includes user-specified third-party Python and R libraries.
  • GitHub Notebooks
    Oracle Machine Learning UI supports direct interaction of Oracle Machine Learning Notebooks with your external GitHub repositories. You can now directly import notebooks from your GitHub repositories.
  • AutoML UI
    AutoML User Interface (AutoML UI) is an Oracle Machine Learning interface that provides you no-code automated machine learning modeling. When you create and run an experiment in AutoML UI, it performs automated algorithm selection, feature selection, and model tuning, thereby enhancing productivity as well as potentially increasing model accuracy and performance.
  • Data Monitoring
    Data Monitoring evaluates how your data evolves over time. It helps you with insights on trends and multivariate dependencies in the data. It also gives you an early warning about data drift.
  • Model Monitoring
    Model monitoring allows you to monitor the quality of model predictions over time and helps you with insights on the causes of model quality issues.

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