A chatbot or robot is a computer program that simulates or provides human-like answers to questions engaging a conversation via auditory or textual input, or both. Chatbots can perform tasks such as data entry and providing information, saving time for users. In recent times, there has been an increased focus on the potential for chatbots to better serve as interfaces between humans and businesses identifying it as a service marketed at solving conversational problems. A chatbot is a computer program that simulates human conversation. It can be used to create automated customer service agents, marketing assistants, and other similar systems.

After creating the machine learning model, we can use the Flask framework to create API for web applications. Here I will teach you how to deploy the flask app to Heroku using docker and GitHub actions. With Docker and Github actions, you can create CI/CD pipeline for your machine learning project. Github action is used to create CI/CD pipeline. CI/CD means continuous integration and continuous deployment.

For starters, deployment is the process of integrating a trained machine learning model into a production environment, usually intended to serve an end-user. Deployment is typically the last stage in the development lifecycle of a machine learning product. The "Model Deployment" stage above consists of a series of steps which are shown in the image below: For the purpose of this tutorial, I will use Flask to build the web application. In this section, let's train the machine learning model we intend to deploy. For simplicity and to not divert from the primary objective of this post, I will deploy a linear regression model.

This repository contains instructions, template source code and examples on how to serve/deploy machine learning models using various frameworks and applications such as Docker, Flask, FastAPI, BentoML, Streamlit, MLflow and even code on how to deploy your machine learning model as an android app. The Repository also has code and how-to's for deploying your apps to various cloud platforms(AWS, Heroku, Vercel etc), working with Github actions for CI/CD(Continuous Integration and Continuous Development), TDD(Test driven development) with pytest and other useful information. Before we get into building and deploying our models we'll have to setup our environment. I use'pyenv' for managing different versions of python and pyenv-virtualenv for setting up virtual environments. You can learn how to install pyenv on your operating system by checking out their official github.

In conclusion to the series where I deployed containers for our ML/DL models for free on Heroku, I will now deploy a web app with 6 Deep Learning models. The Web-App that would be containerized and deployed is Dr. Detect. In the latter half, I will discuss some take-away points related to my experience. The procedure to deploy the web app is the same as discussed in the previous article. There is no difference in the Dockerfile and Requirements.txt

In a previous blog post, I demonstrated how you can build a machine learning web app in Python using the Streamlit library. This is an app that allows users to enter information about their health and lifestyle, and returns an output prediction of how likely the person is to develop heart disease in 10 years. If you'd like to learn more about the model and how the app was built, feel free to go through the tutorial. Otherwise, you can simply navigate to this Github repository and clone it. It contains all the code files necessary to build and deploy the web application.

When the standard error of mean drops the red threshold we have determined, an alert would be sent, which would require us to look at the model performance and take necessary action like retraining. Retraining can be done in two different methods, either manual retraining or automatic retraining; manual retraining is far more common, as most teams are apprehensive about retraining their models without human interference. Next, we would look at a deployment done by me in Heroku using flask and python. I worked on a case study project, to provide a demo of the same, I deployed the machine learning model as a web application. The case study was to predict the abuse category based on the description provided by the victim.

If you're like I am, then you've probably watched and read a number of tutorials on creating machine learning models with TensorFlow, PyTorch, Scikit-Learn or any other framework out there. But there is one thing that these tutorials tend to miss out on, and that's model deployment. In this tutorial, I'll discuss on how to deploy a CNN TensorFlow model that classifies food images to Heroku using FastAPI and Docker. If you're unfamiliar, FastAPI is a Python web framework for creating fast API applications. And in my opinion, it is the easiest to learn out of all the Python web frameworks out there.

This article was published as a part of the Data Science Blogathon. Jupyter Notebook is a web-based interactive computing platform that many data scientists use for data wrangling, data visualization, and prototyping of their Machine Learning models. It is easy to use the platform, and we can do programming in many languages like Python, Julia, R, etc. By default, it comes with Ipython kernels, and if necessary, we can install other language kernels. We'll need more tools to see how our prototype model works in a production environment and how visualizations look in a dashboard because they can only be used to prototype models and do things like Data wrangling and Data Visualization.

Many Data Science / Engineering enthusiasts out there have that dream of successfully deploying there Machine or Deep learning models into a usable Application. I also had this dream and I still do for many of my models yet to be deployed. This then prompted me to learn and map out the necessary pathways to be followed to successfully deploy a Machine Learning model. One other reason for this article is to prevent people from facing some issues I encountered during the course of deploying my model, one of which is the 17-times crashing of the model before the final deployment. This article contains the pathway and important links to articles, free books and videos to aid your model deployment.