Starting on the Advanced Analytics Workspace¶
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:
- Experimentation / Analysis
- 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.
- 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
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.
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
To create any an R-Shiny Dashboard, you just have to submit a GitHub pull request to our R-Dashboards GitHub repository.
If an experiment turns into a product, then one of the following may be needed:
- Kubeflow pipelines for high-volume/intensity work
- Automation 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?¶
- 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.