Deploy an NLP model with Streamlit and Heroku

After finishing a recent Fake News Classification Project, I wanted to build a simple webapp that used my model. Because of their ease of implementation, I chose to build the app with Streamlit and deploy it with Heroku. While there is a lot more that can be done with both platforms, what follows are step by step instructions to get started with a simple project using my app as an example.

Install Streamlit

Please note that Streamlit requires Python 3.6 or later.

pip install streamlit

After installation is complete, test it out the installation with the built in ‘hello world’ app.

streamlit hello

If everything is working ok, this will open a window in a browser with a web page that offers several demos.

You can then return to your terminal window and stop Streamlit with control-c.

#python #fake-news #data-science #heroku #streamlit

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Workshop Alert! Deep Learning Model Deployment & Management

The Association of Data Scientists (AdaSci), the premier global professional body of data science and ML practitioners, has announced a hands-on workshop on deep learning model deployment on February 6, Saturday.

Over the last few years, the applications of deep learning models have increased exponentially, with use cases ranging from automated driving, fraud detection, healthcare, voice assistants, machine translation and text generation.

Typically, when data scientists start machine learning model development, they mostly focus on the algorithms to use, feature engineering process, and hyperparameters to make the model more accurate. However, model deployment is the most critical step in the machine learning pipeline. As a matter of fact, models can only be beneficial to a business if deployed and managed correctly. Model deployment or management is probably the most under discussed topic.

In this workshop, the attendees get to learn about ML lifecycle, from gathering data to the deployment of models. Researchers and data scientists can build a pipeline to log and deploy machine learning models. Alongside, they will be able to learn about the challenges associated with machine learning models in production and handling different toolkits to track and monitor these models once deployed.

#hands on deep learning #machine learning model deployment #machine learning models #model deployment #model deployment workshop

Deploy your first end-to-end ML model using Streamlit

Imagine building a supervised machine learning ML model to decide whether a credit card transaction has detected fraud or not. With the model confidence level in successful applications, we can evaluate the risk-free credit cards transactions. So you have built the model, which can detect credit card frauds, now what? The deployment of such ML-model is the prime goal of the project.

Deploying an ML-model simply means the integration of the model into an existing production environment which can take in an input and return an output that can be used in making practical business decisions. Here is where Streamlit comes to play !

Streamlit is a open-source app framework__is__the easiest way for data scientists and machine learning engineers to create beautiful, performant apps in only a few hours! All in pure Python. All for free.

In the part one of this tutorial I am going to deploy a Supervised machine learning model to predict the age of a Abalone and in the next part of the tutorial we will host this web app on Heroku. An Abalone is a molluscs with a peculiar ear-shaped shell lined of mother of pearl. Abalone’s age can be obtained using their physical measurement. Let us deploy the model.

#machine-learning #supervised-learning #deploy #deployment-model #streamlit

8 Open-Source Tools To Start Your NLP Journey

Teaching machines to understand human context can be a daunting task. With the current evolving landscape, Natural Language Processing (NLP) has turned out to be an extraordinary breakthrough with its advancements in semantic and linguistic knowledge. NLP is vastly leveraged by businesses to build customised chatbots and voice assistants using its optical character and speed recognition techniques along with text simplification.

To address the current requirements of NLP, there are many open-source NLP tools, which are free and flexible enough for developers to customise it according to their needs. Not only these tools will help businesses analyse the required information from the unstructured text but also help in dealing with text analysis problems like classification, word ambiguity, sentiment analysis etc.

Here are eight NLP toolkits, in no particular order, that can help any enthusiast start their journey with Natural language Processing.

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Also Read: Deep Learning-Based Text Analysis Tools NLP Enthusiasts Can Use To Parse Text

1| Natural Language Toolkit (NLTK)

About: Natural Language Toolkit aka NLTK is an open-source platform primarily used for Python programming which analyses human language. The platform has been trained on more than 50 corpora and lexical resources, including multilingual WordNet. Along with that, NLTK also includes many text processing libraries which can be used for text classification tokenisation, parsing, and semantic reasoning, to name a few. The platform is vastly used by students, linguists, educators as well as researchers to analyse text and make meaning out of it.

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#developers corner #learning nlp #natural language processing #natural language processing tools #nlp #nlp career #nlp tools #open source nlp tools #opensource nlp tools

Deploy an NLP model with Streamlit and Heroku

After finishing a recent Fake News Classification Project, I wanted to build a simple webapp that used my model. Because of their ease of implementation, I chose to build the app with Streamlit and deploy it with Heroku. While there is a lot more that can be done with both platforms, what follows are step by step instructions to get started with a simple project using my app as an example.

Install Streamlit

Please note that Streamlit requires Python 3.6 or later.

pip install streamlit

After installation is complete, test it out the installation with the built in ‘hello world’ app.

streamlit hello

If everything is working ok, this will open a window in a browser with a web page that offers several demos.

You can then return to your terminal window and stop Streamlit with control-c.

#python #fake-news #data-science #heroku #streamlit

Deployment Using Streamlit and Heroku

All the code and the screenshots used in this article are from a personal project that I worked on, earlier this year. The code for the GitHub repo is linked here and the deployed model is linked here

BUILDING AN UI USING STREAMLIT:

First, you need to get streamlit installed on your system, or on the virtual environment where you’re working on this project.

If you do not have streamlit installed, open the command prompt and type:

 pip install streamlit

Once you have streamlit installed, you should check out the official documentation of streamlit to familiarize yourself with the wide range of widgets provided by their python library. I will get into the following widget that I felt, are the most useful to start off with :

1. To enter plain text

streamlit.write('Text data')

2. To Create a drop-down list

choices = ['1', '2', '3']
selected_ch = streamlit.selectbox('Pick a number', choices)

#streamlit #heroku #deployment #machine-learning #ui