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How to Develop an End-to-End Machine Learning Project and Deploy it to Heroku with Flask.Here are resources for you to learn how to deploy your model on the Heroku platform.
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On the deployment part, you can upload your trained machine learning model and source code onto Heroku by linking your Github repository to your Heroku account. You can do this right from the command line using the Heroku CLI (available for Windows, Linux, and Mac users). The good thing about Heroku is that it makes it easy to create, deploy and manage your app. It also supports several widely used programming languages like Python, Java, PHP, Node, Go, Ruby, Scala, and Clojure. It offers a wide range of services and tools to speed up your development and helps you avoid starting everything from scratch. If you want to deploy your model for the first time, I recommend that you try Heroku because it is flexible and easy to use It makes it easy to run Python programs in the cloud, and provides an straightforward way to host your web-based Python applications. PythonAnywere is another well-known and growing platform as a service based on the Python programming language. Machine Learning Model Deployment Option #2: PythonAnywhere How to Deploy your NLP Model to Production as an API with Algorithmia.Here’s a good resource for you to learn more about Algorithmia.
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Your application will be free of the concerns of a machine learning environment. In this case, you have to call your model and make predictions as an API call. The good thing about Algorithmia is that it separates Machine Learning concerns from the rest of your application. Now your model can be used for different applications of your choice, such as web apps, mobile apps, or e-commerce, by a simple API call from Algorithmia. It allows users to create code snippets that run the ML model and then host them on Algorithmia. Let’s get started! 🚀 Machine Learning Model Deployment Option #1: AlgorithmiaĪlgorithmia is a MLOps (machine learning operations) tool founded by Diego Oppenheimer and Kenny Daniel that provides a simple and faster way to deploy your machine learning model into production.Īlgorithmia specializes in “algorithms as a service”. It’s also for those who are looking for an alternative platform to deploy their machine learning models. This article is for those who have created a machine learning model in a local machine and want to explore potential platforms for deploying that model. If you wan to get unlimited services you will be charged according to the service’s price. Note: The platforms mentioned in this article provide free tier plans that allow you to use their products or services up to their specified free usage limit. I have also included some great resources to help you start deploying your model on a particular platform. In this article, you will learn about different platforms that can help you deploy your machine learning models into production (for free) and make them useful. Only when a model is fully integrated with the business systems, we can extract real value from its predictions. Then I decided to explore different platforms that were specifically created for machine learning model deployment (or had a good environment to support my model stack).
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