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Getting Started

Starting on the Advanced Analytics Workspace

Advanced Analytics Workspace homepage

The Advanced Analytics Workspace portal is a great place to discover and connect to the available resources we'll be talking about here.

We'll break down the standard tasks into three categories:

  1. Experimentation / Analysis
  2. Publishing
  3. Large scale production

All are important, and we will address all of them, but we'll focus on the first two as these are most widely applicable.

For Experiments

Jupyter notebooks

  • R, Python, and Julia
  • Choose the CPU/RAM you need, big or small, to fit your analysis
  • Share your workspace with your team, along with the data and notebooks within

Jupyter Notebooks

Learn More

Desktops with ML-Workspace

Notebooks are more easily shared than desktops, but we also have the ability to run a full desktop, with typical applications, right inside your browser.

Learn More

For Publishing

R Shiny

R Shiny

The platform is designed to host any kind of open source application you want. We have an R-Shiny server for hosting R-Shiny apps

R Shiny Server

To create any an R-Shiny Dashboard, you just have to submit a GitHub pull request to our R-Dashboards GitHub repository.

For Production

If an experiment turns into a product, then one of the following may be needed:

  • Kubeflow pipelines for high-volume/intensity work
  • Automation pipelines

Kubeflow Pipelines

Ask for help in production

The Advanced Analytics Workspace support staff are happy to help with production oriented use cases, and we can probably save you lots of time. Don't be shy to ask us for help!

How do I get data? How do I submit data?

Browse Datasets

  • Every workspace can be equipped with its own storage.
  • There are also storage buckets to publish datasets; either for internal use or for wider release.

We will give an overview of the technologies here, and in the next sections there will be a more in-depth description of each of them.

Browse some datasets

Browse some datasets here. These data sets are meant to store widely shared data. Either data that has been brought it, or data to be released out as a product. As always, ensure that the data is not sensitive.