--- title: "AI Deploy - Apps portfolio" description: "A collection of AI apps that can be easily deployed with AI Deploy" url: https://docs.ovhcloud.com/es/guides/public-cloud/ai-machine-learning/ai-deploy-apps-portfolio lang: es lastUpdated: 2023-04-04 --- # AI Deploy - Apps portfolio :::info AI Deploy is covered by **[OVHcloud Public Cloud Special Conditions](https://storage.gra.cloud.ovh.net/v1/AUTH_325716a587c64897acbef9a4a4726e38/contracts/d2a208c-Conditions_particulieres_OVH_Stack-WE-9.0.pdf)**. ::: ## Objective 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. ## Portfolio of AI apps and models To test **AI Deploy**, you can quickly deploy apps based on those proposed in our portfolio. ### Quick examples | Owner | Task | Description | Documentation | Dockerfile | Docker image | CLI command | Usage | | -------- | ------------------------------ | ----------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------- | | OVHcloud | Hello world | Launch your first API with Flask | [AI Deploy - Getting started](/es/guides/public-cloud/ai-machine-learning/ai-deploy-getting-started.md) | [Dockerfile - Hello world](https://github.com/ovh/ai-training-examples/blob/main/apps/flask/hello-world/Dockerfile) | `priv-registry.gra.ai.cloud.ovh.net/ai-deploy-portfolio/ai-deploy-hello-world` | `ovhai app run priv-registry.gra.ai.cloud.ovh.net/ai-deploy-portfolio/ai-deploy-hello-world` | **API** - interact with the API with a curl command or a Python script | | OVHcloud | EDA and interactive prediction | Explore, analyse iris data and do interactive prediction with Streamlit | [AI Deploy - Tutorial - Deploy an interactive app for EDA and prediction using Streamlit](/es/guides/public-cloud/ai-machine-learning/ai-deploy-streamlit-eda-iris.md) | [Dockerfile - EDA and prediction on iris data](https://github.com/ovh/ai-training-examples/blob/main/apps/streamlit/eda-classification-iris/Dockerfile) | `priv-registry.gra.ai.cloud.ovh.net/ai-deploy-portfolio/streamlit-eda` | `ovhai app run priv-registry.gra.ai.cloud.ovh.net/ai-deploy-portfolio/streamlit-eda` | **Web interface** - access to the app with the url | | OVHcloud | Sketch recognition | Recognize handwritten digits with Gradio | [AI Deploy - Tutorial - Deploy a Gradio app for sketch recognition](/es/guides/public-cloud/ai-machine-learning/ai-deploy-gradio-sketch-recognition.md) | [Dockerfile - Sketch recognition](https://github.com/ovh/ai-training-examples/blob/main/apps/gradio/sketch-recognition/Dockerfile) | `priv-registry.gra.ai.cloud.ovh.net/ai-deploy-portfolio/gradio-sketch-recognition` | `ovhai app run priv-registry.gra.ai.cloud.ovh.net/ai-deploy-portfolio/gradio-sketch-recognition` | **Web interface** - access to the app with the url | | OVHcloud | Spam classification | Classify spam messages with FastAPI | [AI Deploy - Tutorial - Deploy and call a spam classifier with FastAPI](/es/guides/public-cloud/ai-machine-learning/ai-deploy-fastapi-spam-classifier.md) | [Dockerfile - Spam classifier API](https://github.com/ovh/ai-training-examples/blob/main/apps/fastapi/spam-classifier-api/Dockerfile) | `priv-registry.gra.ai.cloud.ovh.net/ai-deploy-portfolio/fastapi-spam-classification` | `ovhai app run priv-registry.gra.ai.cloud.ovh.net/ai-deploy-portfolio/fastapi-spam-classification` | **API** - interact with the API with `/docs` or curl command | | OVHcloud | Sentiment analysis | Analyse text sentiment with Hugging Face models and Flask | [AI Deploy - Tutorial - Deploy an app for sentiment analysis with Hugging Face and Flask](/es/guides/public-cloud/ai-machine-learning/ai-deploy-flask-hugging-face-sentiment-analysis.md) | [Dockerfile - Sentiment analysis Hugging Face app](https://github.com/ovh/ai-training-examples/blob/main/apps/flask/sentiment-analysis-hugging-face-app/Dockerfile) | `priv-registry.gra.ai.cloud.ovh.net/ai-deploy-portfolio/flask-sentiment-analysis` | `ovhai app run priv-registry.gra.ai.cloud.ovh.net/ai-deploy-portfolio/flask-sentiment-analysis` | **Web interface** - access to the app with the url | | OVHcloud | Speech-to-Text | Use Speech-to-Text powers on audio and video | [AI Deploy - Tutorial - Create and deploy a Speech to Text application using Streamlit](/es/guides/public-cloud/ai-machine-learning/ai-deploy-streamlit-speech-to-text-app.md) | [Dockerfile - Speech-to-Text Streamlit app](https://github.com/ovh/ai-training-examples/blob/main/apps/streamlit/speech-to-text/Dockerfile) | `priv-registry.gra.ai.cloud.ovh.net/ai-deploy-portfolio/streamlit-speech-to-text` | `ovhai app run priv-registry.gra.ai.cloud.ovh.net/ai-deploy-portfolio/streamlit-speech-to-text` | **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**. :::info Each of the following apps launches on port `8080`. You don't need to enter it in the launch command. ::: :::info 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. :::info You will find all the **codes** and **documentation** needed to deploy each app [here](/es/guides/public-cloud/ai-machine-learning/overview.md). ::: Here are some examples of AI apps we propose: - Deploy an app for audio classification task using Streamlit - Deploy a web service for YOLOv5 using Flask - Deploy a Gradio app for sketch recognition - Deploy an app for sentiment analysis with Hugging Face models using Flask - Deploy an interactive app for EDA and prediction using Streamlit - Deploy and call a spam classifier with FastAPI If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/en-gb/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project. ## Feedback Please feel free to send us your questions, feedback and suggestions to help our team improve the service on the OVHcloud [Discord server](https://discord.gg/ovhcloud)