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AI Infrastructure & Serving

Deep Learning In Cloud

Deep Learning In Cloud is a public MIT-licensed GitHub repository that curates cloud GPU providers, model-deployment destinations and MLOps platforms for deep-learning workloads. It organizes links and selected pricing or credit notes for services used to train models, serve them as web applications and manage machine-learning lifecycle work.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Deep Learning In Cloud — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it at the discovery and comparison stage, before choosing a cloud GPU, deployment service or MLOps platform.

Who it is for

  • Deep-learning practitioners looking for cloud GPU resources.
  • Developers seeking services for serving a trained model as a web application.
  • Teams surveying MLOps platforms and cloud machine-learning infrastructure.

Documented uses

  • Find cloud GPU providers for training deep-learning models.
  • Review provider links with listed pricing or credit information.
  • Locate services for deploying a model as a web application.
  • Explore MLOps resources by machine-learning lifecycle needs.
Before you choose

Requirements & limits

The repository is publicly accessible on GitHub and identifies an MIT license. The listed services have their own access, pricing and availability conditions.

  • Whether the resource supports the required stage: training, web deployment or MLOps.
  • The provider pricing or credits shown in the repository, followed by confirmation on the provider’s own website.
  • Whether a listed deployment destination fits a Flask- or Django-style web application.
  • Whether the user needs an index for discovery rather than an infrastructure service or hosted model.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Find a cloud resource for a deep-learning workload

Use the repository to narrow down cloud GPU or model-deployment resources.

Before you start

  • Access to the public GitHub repository.
  • A defined need for GPU training, model deployment or MLOps resources.

Steps

  1. Open the repository and identify the section matching the workload: Cloud GPU Providers, Deploy your model as a Web app, or MLOps Platforms.

  2. Compare the listed provider websites and any pricing or credit information shown in the relevant table.

  3. Open the provider’s linked website to review its current terms and service details.

  4. For web deployment, use the repository’s Flask or Django examples as the documented application context.

Expected outcome

A shortlist of provider websites relevant to the selected deep-learning workload.

Guide limitations

  • The repository is an informational list and does not provide the cloud infrastructure itself.
  • Listed pricing, credits and availability can change and require confirmation on each provider’s website.
  • The repository does not document a complete deployment workflow for a specific provider.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
3 October 2026
Original website title
zszazi/Deep-learning-in-cloud · Official homepage title · retained in its source language
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