Top 10 platforms for deploying ML models for 2023

The top 10 platforms for deploying machine learning models will be discussed in this post. We will offer a thorough overview of each platform, highlighting its essential attributes, advantages, and disadvantages.

This tutorial will assist you in selecting the best platform to deploy your machine learning models successfully and efficiently, regardless of whether you are a data scientist or machine learning engineer.

1. Amazon SageMaker

AWS's (Amazon Web Services) Amazon SageMaker is a fully-managed service for creating, and deploying machine learning models at scale. It supports well-known machine learning frameworks like TensorFlow and PyTorch and offers pre-built algorithms, Jupyter notebooks, and tools for data preparation. Developers and data scientists may rapidly and easily create and deploy customised machine learning models with SageMaker.

2. Microsoft Azure Machine Learning Studio

You may create, train, and deploy machine learning models using the cloud-based integrated development environment (IDE) Microsoft Azure Machine Learning Studio. It has a drag-and-drop interface that makes building models simple and also lets you create custom R or Python code. It also offers a variety of tools for testing, deployment, and data preparation.

3. Google Cloud AI Platform

A cloud-based service called Google Cloud AI Platform enables programmers to create, test, and widely deploy machine learning models. A managed environment for running TensorFlow and other machine learning frameworks, as well as a collection of APIs for supplying predictions, are just a few of the tools and services it offers for managing and deploying models. Additionally, it provides data scientists with a streamlined workflow that makes the process of creating and distributing models easier.

4. IBM Watson Studio

IBM Watson Studio is a cloud-based platform designed by IBM for building, training, and deploying machine learning models for production. It allows teams to work together, and provides tools for automating the deployment process of model. With Watson Studio, you can easily deploy your models for production and make them available via APIs.

5. Heroku

Heroku is a cloud-based platform that allows you to deploy web applications, including machine learning models, without worrying about infrastructure management. It supports various programming languages and frameworks, and provides easy integration with other services such as databases and APIs. Heroku offers a free plan for testing and small applications, and paid plans for production-level applications.

6. Algorithmia

Algorithmia is a cloud-based marketplace for machine learning models that allows programmers to deploy their models in production and make them available via APIs. It provides a secure and scalable platform for hosting and managing models, with built-in support for popular programming languages and frameworks. Algorithmia also offers a range of tools for versioning, testing, and monitoring models in production.

7. Databricks

Databricks is a cloud-based platform for building, training, and deploying machine learning models. It provides a collaborative environment and offers tools for data processing, visualization, and model deployment. With Databricks, you can deploy your machine learning models in production easily and quickly.

8. Hugging Face

Hugging Face is a platform for building and sharing natural language processing models, including transformers. It provides pre-trained models and tools for fine-tuning them on your own data. These models can be easily deployed via an API for use in your applications.

9. Paperspace

Paperspace is a cloud-based platform for machine learning. It provides tools for building, training, and deploying machine learning models. With Paperspace, you can easily deploy your models in the cloud and access them via APIs.

10. FloydHub

FloydHub is a cloud-based platform that simplifies the training and deployment of machine learning models. It offers easy collaboration, built-in version control, and supports popular ML frameworks. You can quickly deploy your trained models on the cloud with just a few clicks.

NOTE:

Some of these platforms are not free and are not recommended for beginners

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