When data scientists are training locally on small datasets, they have to deal with dependency installation, parameterization, and provisioning infrastructure. Once they want to train the model for production on a full load dataset, the complexity increases, and a new configuration must be considered.
Today we're launching a GUI feature in ploomber to solve this issue, by allowing our users to drop notebooks and execute them on the cloud without spinning up clusters or worrying about any infrastructure.
The service is based on our open-source software and have a free-tier that allows to scale multiple models before depleting the quota.
Would love to hear thoughts and impressions of it!
You can also reach out to me directly at [email protected]
Today we're launching a GUI feature in ploomber to solve this issue, by allowing our users to drop notebooks and execute them on the cloud without spinning up clusters or worrying about any infrastructure.
The service is based on our open-source software and have a free-tier that allows to scale multiple models before depleting the quota.
Would love to hear thoughts and impressions of it! You can also reach out to me directly at [email protected]