AI Deploy - Apps portfolio

Last updated 24th February, 2023.

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	Dockerfile - Hello world	priv-registry.gra.training.ai.cloud.ovh.net/ai-deploy-portfolio/ai-deploy-hello-world	ovhai app run priv-registry.gra.training.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	Dockerfile - EDA and prediction on iris data	priv-registry.gra.training.ai.cloud.ovh.net/ai-deploy-portfolio/streamlit-eda	ovhai app run priv-registry.gra.training.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	Dockerfile - Sketch recognition	priv-registry.gra.training.ai.cloud.ovh.net/ai-deploy-portfolio/gradio-sketch-recognition	ovhai app run priv-registry.gra.training.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	Dockerfile - Spam classifier API	priv-registry.gra.training.ai.cloud.ovh.net/ai-deploy-portfolio/fastapi-spam-classification	ovhai app run priv-registry.gra.training.ai.cloud.ovh.net/ai-deploy-portfolio/fastapi-spam-classification	API - interact with the API with <app-url>/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	Dockerfile - Sentiment analysis Hugging Face app	priv-registry.gra.training.ai.cloud.ovh.net/ai-deploy-portfolio/flask-sentiment-analysis	ovhai app run priv-registry.gra.training.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	Dockerfile - Speech-to-Text Streamlit app	priv-registry.gra.training.ai.cloud.ovh.net/ai-deploy-portfolio/streamlit-speech-to-text	ovhai app run priv-registry.gra.training.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.

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
• YOLOv7 repository
• Access and load YOLOv5 trained models from PyTorch Hub

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
• Test DALL-E mini on this website
• Access DALL-E mini model from Hugging Face

Stable Diffusion

Stable Diffusion is Text-to-Image model that generates images from text.

References:

• Stable Diffusion repository
• Stable Diffusion website
• Access Stable Diffusion model from Hugging Face

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

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

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

GPT-2

GPT-2 is a Text Generation model developed by Open AI.

References:

• Find more information about GPT-2 here
• Access GPT-2 model from Hugging Face

BERT

Famous NLP models based on BERT can also be deployed for Text Classification for example.

References:

• Access BERT-based models from Hugging Face

BART

BART-based models can also be deployed for Zero-Shot Classification tasks.

References:

• Access BART-based models from Hugging Face

Go further

You can also refer to our GitHub repository to find examples of AI apps to deploy.

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

Feedback

Please feel free to send us your questions, feedback and suggestions to help our team improve the service on the OVHcloud Discord server

---

---

Inne przewodniki, które mogą Cię zainteresować...