---
title: "AI Deploy - Apps portfolio"
description: "AI Deploy allows you to deploy AI apps or models. To test or use the product, you can build on existing AI models. For example, you can rely on open-source mode"
url: https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-deploy-apps-portfolio
lang: en
---
> For AI agents: the complete documentation index is available at https://docs.us.ovhcloud.com/en/llms.txt, the full documentation bundle is available at https://docs.us.ovhcloud.com/en/llms-full.txt.

# AI Deploy - Apps portfolio

**AI Deploy allows you to deploy AI apps or models. To test or use the product, you can build on existing AI models. For example, you can rely on open-source models or apps.**

**Note:**

AI Deploy is covered by **[OVHcloud Public Cloud Special Conditions](https://us.ovhcloud.com/legal/service-specific-terms/)**.

 

## Portfolio of AI apps and models

To test **AI Deploy**, you can quickly deploy apps based on those proposed in our portfolio.

 

### Quick examples

#### Hello world

- **Description:** Launch your first API with Flask
- **Documentation:** [AI Deploy - Getting started](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-deploy-getting-started.md)
- **Dockerfile:** [Dockerfile - Hello world](https://github.com/ovh/ai-training-examples/blob/main/apps/flask/hello-world/Dockerfile)
- **Docker image:** `ij8m5ic1.c1.va1.container-registry.ovh.us/ai-deploy-portfolio/ai-deploy-hello-world`
- **CLI command:** `ovhai app run ij8m5ic1.c1.va1.container-registry.ovh.us/ai-deploy-portfolio/ai-deploy-hello-world`
- **Usage:** API - interact with the API with a curl command or a Python script

#### EDA and interactive prediction

- **Description:** Explore, analyze iris data, and do interactive prediction with Streamlit
- **Documentation:** [AI Deploy - Tutorial - Deploy an interactive app for EDA and prediction using Streamlit](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-deploy-streamlit-eda-iris.md)
- **Dockerfile:** [Dockerfile - EDA and prediction on iris data](https://github.com/ovh/ai-training-examples/blob/main/apps/streamlit/eda-classification-iris/Dockerfile)
- **Docker image:** `ij8m5ic1.c1.va1.container-registry.ovh.us/ai-deploy-portfolio/streamlit-eda`
- **CLI command:** `ovhai app run ij8m5ic1.c1.va1.container-registry.ovh.us/ai-deploy-portfolio/streamlit-eda`
- **Usage:** Web interface - access to the app with the URL

#### Sketch recognition

- **Description:** Recognize handwritten digits with Gradio
- **Documentation:** [AI Deploy - Tutorial - Deploy a Gradio app for sketch recognition](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-deploy-gradio-sketch-recognition.md)
- **Dockerfile:** [Dockerfile - Sketch recognition](https://github.com/ovh/ai-training-examples/blob/main/apps/gradio/sketch-recognition/Dockerfile)
- **Docker image:** `ij8m5ic1.c1.va1.container-registry.ovh.us/ai-deploy-portfolio/gradio-sketch-recognition`
- **CLI command:** `ovhai app run ij8m5ic1.c1.va1.container-registry.ovh.us/ai-deploy-portfolio/gradio-sketch-recognition`
- **Usage:** Web interface - access to the app with the URL

#### Spam classification

- **Description:** Classify spam messages with FastAPI
- **Documentation:** [AI Deploy - Tutorial - Deploy and call a spam classifier with FastAPI](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-deploy-fastapi-spam-classifier.md)
- **Dockerfile:** [Dockerfile - Spam classifier API](https://github.com/ovh/ai-training-examples/blob/main/apps/fastapi/spam-classifier-api/Dockerfile)
- **Docker image:** `ij8m5ic1.c1.va1.container-registry.ovh.us/ai-deploy-portfolio/fastapi-spam-classification`
- **CLI command:** `ovhai app run ij8m5ic1.c1.va1.container-registry.ovh.us/ai-deploy-portfolio/fastapi-spam-classification`
- **Usage:** API - interact with the API with `<app-url>/docs` or curl command

#### Sentiment analysis

- **Description:** Analyze text sentiment with Hugging Face models and Flask
- **Documentation:** [AI Deploy - Tutorial - Deploy an app for sentiment analysis with Hugging Face and Flask](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-deploy-flask-hugging-face-sentiment-analysis.md)
- **Dockerfile:** [Dockerfile - Sentiment analysis Hugging Face app](https://github.com/ovh/ai-training-examples/blob/main/apps/flask/sentiment-analysis-hugging-face-app/Dockerfile)
- **Docker image:** `ij8m5ic1.c1.va1.container-registry.ovh.us/ai-deploy-portfolio/flask-sentiment-analysis`
- **CLI command:** `ovhai app run ij8m5ic1.c1.va1.container-registry.ovh.us/ai-deploy-portfolio/flask-sentiment-analysis`
- **Usage:** Web interface - access to the app with the URL

#### Speech-to-Text

- **Description:** Use Speech-to-Text powers on audio and video
- **Documentation:** [AI Deploy - Tutorial - Create and deploy a Speech to Text application using Streamlit](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-deploy-streamlit-speech-to-text-app.md)
- **Dockerfile:** [Dockerfile - Speech-to-Text Streamlit app](https://github.com/ovh/ai-training-examples/blob/main/apps/streamlit/speech-to-text/Dockerfile)
- **Docker image:** `ij8m5ic1.c1.va1.container-registry.ovh.us/ai-deploy-portfolio/streamlit-speech-to-text`
- **CLI command:** `ovhai app run ij8m5ic1.c1.va1.container-registry.ovh.us/ai-deploy-portfolio/streamlit-speech-to-text`
- **Usage:** Web interface - access to the app with the URL

If you want to launch these apps from the OVHcloud control panel, fill in the name of the docker image in **Step 2 - Application to deploy**.

**Note:**

Each of the following apps launches on port `8080`. You don't need to enter it in the launch command.

**Note:**

By default, an app is launched with `1 GPU`. However, you can customize the resources you wish to use.

 

## Build you own apps and models to deploy

Below are examples of existing models that can be deployed with **AI Deploy**. However, you are free to deploy any other AI model or app of your choice.

### YOLO

**YOLO** ('You only look once'), is an `Object Detection` algorithms family.
_References:_
- YOLOv5 [repository](https://github.com/ultralytics/yolov5)
- YOLOv7 [repository](https://github.com/WongKinYiu/yolov7)
- Access and load YOLOv5 trained models from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/)

### DALL-E mini

**DALL-E mini** is an AI model that can draw images from any text prompt (`Text-to-Image`).
_References:_
- DALL-E mini [repository](https://github.com/borisdayma/dalle-mini)
- Test DALL-E mini on this [website](https://www.craiyon.com/)
- Access DALL-E mini model from [Hugging Face](https://huggingface.co/dalle-mini)

### Stable Diffusion

**Stable Diffusion** is `Text-to-Image` model that generates images from text.
_References:_
- Stable Diffusion [repository](https://github.com/CompVis/stable-diffusion)
- Stable Diffusion [website](https://stability.ai/blog/stable-diffusion-public-release)
- Access Stable Diffusion model from [Hugging Face](https://huggingface.co/spaces/stabilityai/stable-diffusion)

### EfficientNet

**EfficientNet** is a family of `Image Classification` models. There are eight different EfficientNet models (`b0` -> `b7`)
_References:_
- Access and load EfficientNet model from [PyTorch Hub](https://pytorch.org/hub/nvidia_deeplearningexamples_efficientnet/)

### ResNet

**ResNet** are AI models based residual neural network whose aim is to solve `Image Classification` tasks.
_References:_
- Access and load ResNet models from [PyTorch Hub](https://pytorch.org/hub/pytorch_vision_resnet/)

### MobileNet V2

**MobileNet** are `Computer Vision` models designed to be used in mobile applications. They are known for their small size and low latency.
_References:_
- Access and load ResNet models from [PyTorch Hub](https://pytorch.org/hub/pytorch_vision_mobilenet_v2/)

### GPT-2

**GPT-2** is a `Text Generation` model developed by Open AI.
_References:_
- Find more information about GPT-2 [here](https://en.wikipedia.org/wiki/GPT-2)
- Access GPT-2 model from [Hugging Face](https://huggingface.co/docs/transformers/main/en/model_doc/gpt2)

### BERT

Famous NLP models based on **BERT** can also be deployed for `Text Classification` for example.
_References:_
- Access BERT-based models from [Hugging Face](https://huggingface.co/docs/transformers/main/en/model_doc/bert)

### BART

**BART**-based models can also be deployed for `Zero-Shot Classification` tasks.
_References:_
- Access BART-based models from [Hugging Face](https://huggingface.co/docs/transformers/main/en/model_doc/bart)

 

## Go further

You can also refer to our [GitHub repository](https://github.com/ovh/ai-training-examples) to find examples of AI apps to deploy.

Browse all the [AI & Machine Learning guides](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/landing-page-ai-machine-learning.md).

Browse the rest of the [OVHcloud US documentation](https://docs.us.ovhcloud.com/en/index.md) for guides on our other products and services.

Learn more about [AI & Machine Learning](https://us.ovhcloud.com/public-cloud/ai-machine-learning/) on the OVHcloud US website.

If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://us.ovhcloud.com/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project.