8 FREE Platforms to Host Machine Learning Models

Deploying a machine learning model is one of the most critical steps in setting up an AI project. Whether it’s a prototype or you are scaling it for production, model deployment in ML ensures that the models are accessible and can be used in practical environments. In this article, we’ll explore the best platforms to deploy machine learning models, especially those that allow us to host ML models for free with minimal setup.  

What Are Machine Learning Models?

Machine Learning models are programs that understand the hidden patterns in data to make predictions or combine similar data points. They are the mathematical functions that are trained on historical data. Once the training is completed, the saved model weight file can easily identify patterns, classify information, detect anomalies, or, in certain cases, even generate content. So, data scientists use different machine learning algorithms as the basis for models. As data is introduced to a specific algorithm, it is modified to handle a particular task, which helps to create even better machine learning models.

For example, a decision tree is a common algorithm for both classification and prediction modelling. A data scientist seeking to develop a machine-learning model that identifies different animal species may train a decision tree algorithm using various animal images. Over time, the algorithm would become modified by the data and increasingly better at classifying animal images. In turn, this would eventually become a machine-learning model. 

Top Platforms to Host Machine Learning Models

Building a Machine Learning model genuinely only takes half of the time; the other half lies in making it accessible so others can try out what you have built. So, hosting models on cloud services solves the issue that you don’t have to run them on your local machine. So in this section, we’ll be exploring the leading free platforms for hosting machine learning models, detailing their features and benefits.

1. Hugging Face Spaces

The hugging face spaces, or in short, hf-spaces, is a community-centric platform that allows users to deploy their machine learning models using popular libraries. The spaces allow for hosting the model with a few lines of code, and the public usage is completely free with access to a shared CPU and GPU environment.

Key features of Hugging Face Spaces

• Free to use with built-in support for Python.
• It also offers flexibility in choosing computational resources based on model requirements.
• Provides a platform for collaborators and great community engagement.

Streamlit provides a free cloud platform that helps developers deploy Streamlit applications directly from GitHub repositories. It provides free hosting with basic resources, making it ideal for making dashboards and ML inference apps. It is developed for the quick and easy sharing of data applications.

Key features of Streamlit Community Cloud

• Provides easy deployment with GitHub repositories.
• No server setup is required, hence it reduces resource overhead.
• It also simplifies the deployment process and makes it accessible to non-experts in model deployments.

3. Gradio

Gradio is both a Python library and a hosting platform for quickly creating web UI applications for machine learning models. This makes the applications accessible for users without expertise in web development. It’s used for creating shareable demos with interactive dashboards and data applications.

Key features of Gradio

• It provides access to machine learning models by providing user-friendly interfaces.
• It also supports seamless integration with Hugging Face Spaces for hosting.
• Allows developers to share models without building custom web applications.

4. PythonAnywhere

PythonAnywhere is a cloud-based platform for hosting and developing Python applications. It allows developers to run Python scripts. So, developers who want to deploy and execute their code without using their local servers to set up web applications with Flask and Django.

Key features of PythonAnywhere

• PythonAnywhere offers easy integration with databases like MySQL, making it ideal for hosting applications with backend databases.
• It is ideal for showcasing the prototype applications because it doesn’t need to set up a local Python environment. This makes it perfect for beginners or those who want to show a quick prototype.
• This platform has built-in support for task scheduling Python scripts to run at specific times.

5. MLflow

MLflow is an open-source platform that manages the complete lifecycle of a machine learning project, starting from experimentation to deployment. While it doesn’t provide the direct hosting infrastructure, MLflow models can be deployed to cloud platforms easily using MLflow’s built-in servers. 

Key features of MLflow

• MLflow helps in keeping track of the model’s performance, model registry, and version control.
• Enables developers to have team collaboration in enterprise environments via maintaining logs and comparing them with multiple runs of their ML models.
• Easily integrates with machine learning libraries and other support tools.

6. DagsHub

DagsHub is a collaboration platform built specifically for machine learning projects. It combines Git (for version control), DVC (for data and model verification), and MLflow (for experiment tracking). We can manage datasets, notebooks, and models, and track your ML lifecycle in one place.

Key features of DagsHub

• It allows seamless and easy collaboration for sharing of datasets, models, and experiments, which makes it easy for developers to collaborate and organize work environments.
• It also offers built-in visualization tools for monitoring the model performance and comparing metrics across different experiments.
• DagsHub supports open-source components, making it flexible for further customizations and also helps in expanding its functionality, specifically for users’ needs.

7. Kubeflow

Kubeflow is an open-source platform designed specifically to simplify the deployment, monitoring, and management of machine learning models or workflows on Kubernetes. It aims to provide end-to-end support for the entire machine learning lifecycle, from data preparation to model training to deployment and monitoring in production. Kubeflow allows scalable, distributed, and portable ML workflows.

Key features of Kubeflow

• Facilitates easy deployment of machine learning models into production as it enables easy and seamless integration with Kubernetes for automated scaling and management.
• It also supports popular machine learning frameworks such as Tensorflow, PyTorch, MXNet, and others, allowing developers to work with their preferred tools.
• Kubeflow allows you to define machine learning pipelines as code using Python. This enables easy versioning, testing, and sharing of workflows.

8. Render

Render is a cloud platform that gives a unified solution for deploying and managing web applications, APIs, and static websites. It simplifies the process of hosting full-stack applications. This offers automatic scaling, continuous deployment, and easy integration with popular databases. Render is designed to provide a simple and developer-friendly alternative to traditional cloud providers with a major focus on ease of use, speed, and efficiency for small and enterprise applications.

Key features of Render

• Render offers easy integration with GitHub and GitLab, which allows automatic deployments whenever changes are pushed to repositories and ensures continuous deployment with minimal setup.
• It automatically scales the applications up and down based on traffic, and ensures performance is optimized without manual intervention.
• Render also provides real-time logs, performance monitoring, and alerts to keep track of the application’s performance. Also, it can be integrated with GitHub Actions for customized deployment pipelines and workflows.

Comparison Between the Platforms

Why Host Machine Learning Models?

Once you have trained your machine learning model and tested it on the sample data you have, as test data, now it’s time to host it on a suitable platform that meets the project’s needs to make it usable in real-time scenarios. Whether the final goal of the model is to do predictions via API’s, or embed the models into web applications. Hosting the model ensures that our model is accessible and operational to others.

What Makes Hosting the Model Essential:

• Accessibility and Interactivity: Hosting models allow users or other applications based on top of the hosted model to interact with the model from anywhere via APIs.
• Scalability: Also, most of the hosting platforms often provide the scaling that helps the model to handle multiple users’ requests at the same time and ensures that its performance doesn’t fall off.
• Collaboration: Also, the hosted models can easily be shared with teams or with the broader community for feedback and more reliable integration.
• Monitoring and Maintenance: By hosting the model, one can easily monitor the logging, versioning, and monitoring tools help to keep the model performance up to date.
• Integration: The hosted model can be easily integrated with databases, front-end applications, or other APIs for seamless pipeline management.

Conclusion

The life cycle of Machine Learning isn’t over till the models are used in the real world. So, choosing the right platform to host your machine learning model is a very crucial step of this life cycle, depending on the project’s size and technical requirements. Therefore, if you are looking for quick demos with minimal setup, platforms like HuggingFace Spaces, Streamlit, and Gradio are some of the best starting points. For more advanced workflows for the production environment deployment, Render, KubeFlow, and MLflow offer scalability and version control as per your needs. Moreover, platforms like PythonAnywhere and Dagshub are ideal for small projects and team collaborations. 

So, whether you are a student, a data science enthusiast, or a working professional, these platforms will support your ML journey from prototype to production of your model.

Hello! I’m Vipin, a passionate data science and machine learning enthusiast with a strong foundation in data analysis, machine learning algorithms, and programming. I have hands-on experience in building models, managing messy data, and solving real-world problems. My goal is to apply data-driven insights to create practical solutions that drive results. I’m eager to contribute my skills in a collaborative environment while continuing to learn and grow in the fields of Data Science, Machine Learning, and NLP.