Hi everyone, and welcome back to unit seven of the SAP Data Intelligence for Enterprise AI course.
In this unit, we will focus on the SAP Data Intelligence's capabilities
that are available to you through the Jupyter Notebook. As you saw in the demo in the previous unit,
through the ML Scenario Manager you can create multiple notebooks.
Once you click on a notebook, it will automatically take you to your Jupyter Lab,
where you can choose your Python kernel on which you want to run your code.
Through the Jupyter Notebooks, you can manage multiple files.
You can import your Python notebook or file, read from S3 or Semantic Data Lake,
install required libraries with pip install. Here you can see how to export the result notebook.
You can also export your notebook as a Data Hub file, which has the APIs required to run the Python code
on the pipeline modeler, or simply as an executable script. We also have an SAP Python SDK for Data Intelligence,
which provides convenient, programmatic access to SAP Data Intelligence functionalities.
For example, it helps you to create and access ML scenarios and their versions,
as well as to retrieve and update metadata, create configurations, and start executions and deployments,
create and update pipelines, and bind them to ML scenarios, access artifacts from training containers,
and report metrics through the ML Tracking SDK. By using the Python SDK for Data Intelligence,
you can access a registered dataset only by referring to its technical identifier.
Also, you can use this SDK to create a pipeline, while passing your Python code to your Python operator,
and that way expedite the process of moving from the PoC to a product.
In the picture, you can see the pipeline, which is created via the Jupyter Notebook
and through the Python SDK for DI. You can also run SAP PAL, Predictive Analysis Library,
through the SAP Data Intelligence's Jupyter Notebook. When your data resides in HANA,
it doesn't make sense to move your data out of it just to run machine learning algorithms on it.
We can use PAL or APL to push down the executions to HANA. In chapter four, we explained about APL and PAL,
and in the previous unit, you saw a demo of using PAL to run random forest, to make the prediction
about book returns using their genre. Here, you can see some of the algorithms
that are provided to you via PAL. As you can see, there is an extensive list of algorithms
for classification, regression, cluster analysis, recommenders, and so on.
For more information, please refer to the SAP PAL documentation.
Here, you can see an example of how to run PAL via the Jupyter Notebook.
What you have to do is import the hana_ml and hana_ml.algorithms.pal libraries first.
The HANA and PAL capabilities will then be available to you through various interfaces developed for you.
For instance, here you can see how you can easily make a connection to a HANA table,
and pass the required parameters to the random forest algorithm.
In chapter four, we also talked about the APL, Automated Predictive Library, of HANA,
which provides automated classification, regression, clustering, time series,
and descriptive analytics within HANA. With APL functions, you can create your models,
train your models, apply your models, and also query your models.
For instance, retrieve variable descriptions, indicators, reporting information, and so on.
In this slide, you can see how to import the HANA ML, APL library.
For more information on the available algorithms in APL, parameters, and how to use them in the Jupyter Notebook,
please refer to the help documentation for APL. Hope you enjoyed this unit.
In this unit, we focused on the capabilities that are available to you through the Jupyter Notebook.
For instance, how to use a Python SDK for Data Intelligence, or run APL and PAL, and how to trigger their executions
through the SAP Data Intelligence's Jupyter Notebook. In the next unit, we will talk more about
the SAP Data Intelligence's pipeline modeler. All the best for your self-assessment quiz,
and see you in the next unit.