How to Easily Deploy Machine Learning Models Using Flask

 By KDnuggets - 2021-01-05

Description

This post aims to make you get started with putting your trained machine learning models into production using Flask API.

Summary

  • When a data scientist/machine learning engineer develops a machine learning model using Scikit-Learn, TensorFlow, Keras, PyTorch etc, the ultimate goal is to make it available in production.
  • model.py — This contains code for the machine learning model to predict sales in the third month based on the sales in the first two months.
  • It displays the returned sales value in the third month.
  • One can use the knowledge gained in this blog to make some cool models and take them into production so that others can appreciate their work.

 

Topics

  1. Machine_Learning (0.36)
  2. Backend (0.15)
  3. NLP (0.14)

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