In case you are catching the train running, here is the link to the introduction blog of the Machine Learning in a Box series which allow you to get the series from the start. At the end of this introduction blog you will find the links for each elements of the series.

Before we get started, a quick recap from last time

Last time, you got access to not one, not two, not three but four tutorial groups helping you to start building machine learning models using real data and algorithms.

Here are the links for reference:

• Build an Application using SAP HANA, express edition XS Advanced Development Model
  • For Movie Recommendation
  • For Time Series Forecasting
• Build a Model using SAP HANA, express edition Predictive libraries and SQL
  • For Movie Recommendation
  • For Time Series Forecasting

There should be no more secret on how you can use an AFL function to run one of the predictive algorithms available with SAP HANA.

Let’s now look at this new post.

Welcome to part 10 of Machine Learning in a Box!

JupyterLab

In part 7 of this blog series, I introduced a new tool called Jupyter Notebook, one of my favorite go-to tool when dealing with data science activities.

Just as a refresher, Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text.

In a nutshell, a realm of endless possibilities at your fingertip.

So, why would you look at JupyterLab, the reason is simple: JupyterLab is the next-generation web-based user interface for Project Jupyter.

It doesn’t mean that your notebooks are useless, and you will need to code them again, not at all in fact.

Let’s dive into that!

JupyterLab

JupyterLab enables you to work with documents like you did with Jupyter Notebooks and use kernels etc. but that’s not it!

It also allows you to use text editors, terminals, and custom components in a flexible, integrated, and extensible manner.

For example, the Code Consoles provides you a transient scratchpads for running code interactively, with full support for rich output.

JupyterLab also offers a unified model for viewing and handling data formats (images, CSV, JSON, Markdown, PDF, Vega, Vega-Lite, etc.) and can also display rich kernel output in these formats.

JupyterLab is served from the same server and uses the same notebook document format as the classic Jupyter Notebook.

Installing JupyterLab

Again, just like with Jupyter Notebook, you can install JupyterLab both locally on your machine or on your SAP HANA, express edition server.

The installation is pretty straight forward if you choose a local installation, you can refer to the online installation guide.

Now, if you decide to setup JupyterLab on your SAP HANA, express edition, you will need to consider a few things which I have documented in a tutorial.

For example, you will need to generate a configuration file and enable the use of the machine IP address instead of localhost, in addition to set up SSL (optional) and a password (optional).

As usual, I produce a tutorial that guide you through the installation, the configuration and the connectivity to you SAP HANA, express edition instance. Here is the link:

•  Use JupyterLab with SAP HANA, express edition

Feel free to use the “provide feedback” link in the tutorial to let me know what you think about it.

Conclusion

I hope I did enough teasing for you to get tempted and start using JupyterLab from now on. Anyway, if you want to learn more about it you can check the JupyterLab announcement blog.

On my side I will do my best to provide you with notebooks in the future with content I produce.

(Remember sharing && giving feedback is caring!)

UPDATE: Here are the links to all the Machine Learning in a Box weekly blogs:

• Introducing “Project: Machine Learning in a Box”
• Machine Learning in a Box (part 2) : Project Methodologies
• Recap Machine Learning in a Box (part 2) : Project Methodologies
• Machine Learning in a Box (part 3) : Algorithms Learning Styles
• Machine Learning in a Box (part 4) : Get your environment up and running
• Machine Learning in a Box (part 5) : Upload Machine Learning Datasets
• Machine Learning in a Box (part 6) : SAP HANA R Integration
• Machine Learning in a Box (part 7) : Jupyter Notebook
• Machine Learning in a Box (part 8) : SAP HANA EML and TensorFlow Integration
• Machine Learning in a Box (part 9) : Build your first Machine Learning application
• Machine Learning in a Box (part 10) : JupyterLab