How to Build and Deploy a Machine Learning model using Docker

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

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  • docker

    Docker - the open-source application container engine (by microsoft)

  • Docker Documentation

  • streamlit

    Streamlit — A faster way to build and share data apps.

  • Streamlit Documentation

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

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  • scikit-learn

    scikit-learn: machine learning in Python

  • Scikit-learn Documentation

  • Pandas

    Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

  • Pandas

  • NumPy

    The fundamental package for scientific computing with Python.

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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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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