6 Best Platform to Deploy Data Science/ML Projects For Free

There are several options available for deploying your ML projects for free. Here are some popular ones:

Heroku: Heroku is a cloud platform that allows you to deploy, manage, and scale your applications. It supports various programming languages and frameworks, including Python, which is commonly used for machine learning. Heroku provides a free tier that allows you to deploy up to five applications with limited resources.

Google Cloud Platform (GCP): GCP provides a free tier that allows you to use several of its services for free, including Google Compute Engine, which can be used to deploy machine learning models. You can use GCP to deploy models built with TensorFlow, Keras, and other popular ML libraries.

Amazon Web Services (AWS): AWS offers a free tier that includes several services, including Amazon SageMaker, which is a fully-managed machine learning service that allows you to build, train, and deploy models. You can deploy your ML models on AWS Lambda or Elastic Beanstalk, which are also included in the free tier.

PythonAnywhere: PythonAnywhere is a cloud-based platform that allows you to develop, run, and host Python applications. It provides a free account that includes 512 MB of storage and allows you to run a single web application.

Microsoft Azure: Microsoft Azure offers a free tier that includes several services, including Azure Machine Learning, which is a cloud-based machine learning platform. You can use Azure to deploy your ML models as web services or Docker containers.

Streamlit: This is one of the best platform to deploy ML apps for free, additional benefit is that it doesn't have any limit on storage and numbers of deployement per user for free. You can deploy your apps directly from GitHub, the setup is done automatically provied your codes must be raw python file with extension .py.

Here is an examples how does my apps look over streamlit cloud:

link:https://iamvivekanand-ml-myprojects022-bmi-calcbmi-calc-alfd9f.streamlit.app/

These are just a few of the many options available for deploying your ML projects for free. Each platform has its strengths and weaknesses, so it's important to choose one that best fits your needs.