In this section we'll cover Jupyter notebook-based development with Dotscience, from setting up your model to deployment and monitoring.
If you have arbitrary code you need to run, you can use the ds command-line tool to integrate it with Dotscience.
If you want to develop Python scripts for model training – as opposed to Jupyter notebooks – the best way to do that is by using the Dotscience Python library in 'remote' mode, known as Dotscience Anywhere.
Learn how you can trace from a model to its training data and back from that to the raw data
Dotscience enables users to collaborate and offer feedback directly on individual runs.
This tutorial demonstrates Dotscience S3 integration with readonly datasets.
Dotscience Git integration allows you to synchronize GitHub repositories with Dotscience filesystems during the run.
We explore how data engineering scripts can be instrumented with functions from the Dotscience python library to enable tracking and provenance.
In this section we'll demonstrate how you can build and deploy scikit-learn models with Dotscience.
Jupyter Notebooks are notoriously hard to use well with Git and GitHub. Dotscience lets you fork someone else's project, create new runs in notebooks and propose them back along with their metrics. See a full, clear full notebook diff and merge conflicting changes with ease.
We look at how you can use Dotscience to explore relationships between hyperparameters & metrics
We demonstrate how you can use Dotscience to provision a dashboard to monitor your models in production.
Automation flows built with visual pipelines
Dotscience allows you to hook into GitLab to give you more control over your model builds.