Import files and data sources to the Platform

There are several ways to import datasets into your project:

File import

You can upload existing files with the data you want to use to the platform in several ways:

  • Upload files directly from your local computer.
    Click Choose files and select your file or just drag and drop them in the dotted area.
    This should be limited to files of 5 GB or smaller, to limit the risk of connection issues during upload.

  • Import data from a URL.
    Click URL import, if your files are hosted online.
    This is recommended for large files, as it usually provides a better and faster connection.

  • Data API You can also use the Data API to let a script (or program) upload the files you want into a new dataset.

Uploading several files

You can upload several files into the same dataset. Uploading several files can be useful if your training examples have several features which are saved in different files.

Rules
If you want to upload multiple files, keep in mind that:

  • All files must be uploaded at the time when the dataset is created.

  • The first file you upload determine the number of examples (rows).

  • Additional files will add features (columns) to the already existing examples (rows).

  • You cannot add more examples by adding more files.

  • All files must contain the same number of examples.

  • The examples must be in the same order in all files.

Data library: ready-made datasets

The Data library contains datasets that are ready to be added to your project.

In the Datasets overview page, click the Data library button to open the Data library window to:

  • Quickly import the data required to follow one of the tutorials

  • Get started training and evaluating models using reference datasets

  • Prototype models on data similar to your own, and use transfer learning when you are ready to train on your own data

Note
Disclaimer
Please note that datasets, machine-learning models, weights, topologies, research papers and other content, including open source software, (collectively referred to as Content) provided and/or suggested by Peltarion for use in the Platform and otherwise, may be subject to separate third party terms of use or license terms. You are solely responsible for complying with the applicable terms. Peltarion makes no representations or warranties about Content. You expressly relieve us from any and all liability, loss or risk arising (directly or indirectly) from Your use of any third party content.

The datasets in the data library may come from third party sources and are provided for convenience. Read the dataset licenses to know the particular terms of each dataset.

Data warehouse: import datasets from Azure Synapse and BigQuery

If you have data stored in Azure Synapse or BigQuery online storage, you can import it to the Platform and start training models on it.
This is done easily in three steps: connect to your data warehouse, select the dataset you want to import, preview the content to make sure it’s correct, and that’s it.

The exact steps depend on your service provider. Currently we support:

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