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Cloud resources for Machine Learning and Deep Learning Models

Aman Anand

Machine Learning requires a lot of resources to train its models, and since the average person rarely has a device with such high configurations, Cloud is the best option. Deep Learning requires more resources than traditional Machine learning models (e.g., SVM, Decision Trees, Random Forests, etc.) because it performs Feature Extraction along with Training of models. The best way to run Deep Learning models is to host them on Cloud and rent the resources. Some of the most famous and best resources for running Machine Learning and Deep Learning models are listed below.

Google Colaboratory

Google Colab is one of the simplest ways to run Machine Learning models. It allows you to create Jupyter notebooks and choose between CPU, GPU, and TPU for hardware.

Kaggle Kernels

Kaggle is the best community for Data scientists – it provides Mini-Courses to learn DataScience and Kernels to run models. Kaggle kernels (now known as notebooks) was essentially based on Google Colab and is now owned by Google.

Paperspace

Paperspace is another very good way to host and run your ML models. It is very easy to deploy the models and it provides MLOps options.

Vast.ai

Vast.ai is a GPU rental platform where you can rent GPUs hourly based on your requirement. The cost for this rental platform is flexible.

Oracle Cloud

This is the official cloud platform of Oracle. A person needs some experience in order to deploy their application here; however, Oracle can be used for many purposes other than Deep Learning and Machine Learning.

Amazon Web Services (AWS)

AWS, as it is popularly known, is the most popular Cloud Hosting platform. It is multipurpose and can be used for other tasks.

Microsoft Azure

Microsoft Azure is the Microsoft alternative of AWS. You will need some training before you can run your application or train your models on this cloud database.

Google Cloud Platform (GCP)

GCP is another Google option for hosting on Cloud that is not limited to ML and DL. It can be thought of as the alternative to AWS.

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machine learning
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deep learning
community creator

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