README.md

Examples

Iris classification: deploy a model to classify iris flowers.
Text generation: deploy OpenAI's GPT-2 to generate text.
Sentiment analysis: deploy a BERT model for sentiment analysis.
Image classification: deploy an Inception model to classify images.
Image classification: deploy a ResNet50 model to classify images.
License plate reader: deploy a YOLOv3 model (and others) to identify license plates in real time.
Multi-model classification: deploy 3 models (ResNet50, Iris, Inception) in a single API.

Denoisify text documents: deploy an Autoencoder model to clean text document images of noise.

Iris classification: deploy a model to classify iris flowers.
Text generation: deploy Hugging Face's DistilGPT2 model to generate text.
Sentiment analysis: deploy a Hugging Face transformers model for sentiment analysis.
Search completion: deploy a Facebook's RoBERTa model to complete search terms.
Answer generation: deploy Microsoft's DialoGPT model to answer questions.
Text summarization: deploy a BART model (from Hugging Face's transformers library) to summarize text.
Reading comprehension: deploy an AllenNLP model for reading comprehension.
Language identification: deploy a fastText model to identify languages.
Multi-model text analysis: deploy 2 models (Sentiment and Summarization analyzers) in a single API.
Image classification: deploy an AlexNet model from TorchVision to classify images.
Image classification: deploy a ResNet50 model from TorchVision to classify images.
Object detection: deploy a Faster R-CNN model from TorchVision to detect objects in images.

Iris classification: deploy an XGBoost model (exported in ONNX) to classify iris flowers.
Multi-model classification: deploy 3 models (ResNet50, MobileNet, ShuffleNet) in a single API.