Hopsworks is a Stockholm-based SME and the HEAP technical work package leader. HEAP researchers are using Hopsworks’ collaborative Machine Learning (ML) platform to build and manage features, training and inference pipelines to analyse sensitive medical data from biological samples and health registries.

Find out how Hopsworks makes their work possible in the “Key Features” video.

Saving weeks of valuable research time

Using HPV-meta, a data-parallel pipeline, a team from Karolinska Institute reduced the time needed to analyse biospecimen data from the Swedish cervical screening cohort study from a month to a week. The team now plan to process hundreds of TB of DNA sequence data. This milestone is already covered in scientific publications, and the open-source code is accessible on GitHub.

Watch the video to find out more about the project from our HEAP researcher Sara.

Start your Machine Learning journey here

For those getting started with Machine Learning, Hopsworks has developed The BIG Dictionary of MLOps.  Covering the entire ML System lifecycle, it explains key principles like observability, automated testing, and versioning of ML artifacts and demystifies the world of MLOps, data engineering, and feature stores.

And for a clear explanation of the various ML feature types, Hopsworks’  blog is an essential go-to resource.

To find out how to get started with Machine Learning by creating a free account in Hopsworks, see our YouTube Playlist.

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