Deploying ML models straight from Jupyter Notebooks

This page summarizes the projects mentioned and recommended in the original post on dev.to

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  • streamlit

    Streamlit — The fastest way to build data apps in Python

    Our part of magic is done, now to the single command deployment I promised in the beginning. Before we go rogue and deploy it to the cloud, let’s run a Streamlit app locally to test things out:

  • flyctl

    Command line tools for fly.io services

    Once we finished playing around with the model locally, let’s cast our final spell for the day 🧙‍♂️ and deploy the model to fly.io:

  • InfluxDB

    Access the most powerful time series database as a service. Ingest, store, & analyze all types of time series data in a fully-managed, purpose-built database. Keep data forever with low-cost storage and superior data compression.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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