Month: December 2019

serving ml models as api

Serving Machine Learning Models as API with FastAPI

In a previous post, we learnt about the various ways we can productionize our machine learning models. In this tutorial we will be looking at how to serve our machine learning models as an API- which is one of the ways to productionize your ML model. There are several ways we can achieve this. We …

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Building A Drag & Drop Machine Learning App with Streamlit

Building Machine learning models can be time consuming and tedious,but with the advent of Auto ML services and application we can reduce our burden . In this tutorial we will be building a Semi – Automated ML app with streamlit. It is going to be a drag and drop machine learning app that features  Exploratory …

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Building and Publishing Packages with Poetry

In this tutorial we will be looking at a nice python tool for building and publishing python packages and libraries. Poetry is a python dependency management tool that enables us to manage several python dependencies on your system. It comes with the ability to create virtual environment just like pipenv. Just like pipenv, it manages …

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Building A Document Redactor NLP App with Streamlit,Spacy and Python

In this tutorial we will be building a document redaction app. Document redaction refers to the process of sanitizing and censoring a document based on selected terms. It is one of the applications of NLP – natural language processing. This image shows some of it use. In this case the important terms have been blacked …

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How to Use Displacy in Flask For Named Entity Recognition

In this tutorial we will be see how to render the named entities of a text extracted using spacy’s displacy in flask. First of all the basic requirement is to use the following packages Flask Spacy Flask-Markdown To install the various packages we will be using pip as below pip install flask Flask-Markdown spacy To …

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