Machine Learning Apps

Machine Learning with PyCaret in Python

In this fourth industrial age – the Age of AI, the knowledge and applications of machine learning is essential for every business and industry. It has a lot of potential and benefit when incorporated into your business . But not everyone has the time and the energy to learn the intricacies of Data Science and …

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How to Deploy A Face Detection Streamlit App on Heroku

In the previous tutorial,we saw how to build a simple face detection and image manipulation app with streamlit and opencv in python.   In this tutorial we will see how to deploy our app on heroku.  Deploying apps on heroku is quite simple, but you can face some issues when trying to deploy a computer …

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Building A Face Detection App with Streamlit and OpenCV

In this tutorial we will be building a simple face detection app with streamlit  and OpenCV. We will be using streamlit to help us with our UI. This is going to be a simple Computer Vision App using OpenCV in python. This is how our app will look like after we are done building it. …

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Simple Machine Learning App with Streamlit using Car Evaluation Dataset

In this tutorial we will be building a simple ML app with the awesome ML framework -Streamlit. We will be using the car evaluation data set from here. Let us see the basic workflow we will be using for this simple project. Building the ML Model Interpreting the Model Building the ML App with Streamlit …

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How to Deploy Streamlit Apps to Google Cloud Platform(GCP App Engine)

In the previous tutorial we saw how to deploy streamlit using docker. In this tutorial we will be looking at how to deploy our streamlit app to Google Cloud Platform App Engine using docker. Docker gives you the possibility to deploy apps on any available cloud platform such as GCP,Azure,AWS ,Alibabacloud etc. The main requirements …

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How to Deploy Streamlit Apps with Docker

In a previous tutorial we learnt about the various ways of productionizing and deploying machine learning models and one of the ways is to use docker. In this tutorial we will be using docker to deploy our streamlit app. What is Docker? Docker is a platform for developers and sysadmins to build, share and run …

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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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