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Iris classification: deploy a model to classify iris flowers.
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Text generation: deploy OpenAI's GPT-2 to generate text.
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Sentiment analysis: deploy a BERT model for sentiment analysis.
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Image classification: deploy an Inception model to classify images.
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Image classification: deploy a ResNet50 model to classify images.
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License plate reader: deploy a YOLOv3 model (and others) to identify license plates in real time.
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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.
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Iris classification: deploy a model to classify iris flowers.
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Text generation: deploy Hugging Face's GPT-2 model to generate text.
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Sentiment analysis: deploy a Hugging Face transformers model for sentiment analysis.
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Search completion: deploy a Facebook's RoBERTa model to complete search terms.
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Answer generation: deploy Microsoft's DialoGPT model to answer questions.
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Text summarization: deploy a BART model (from Hugging Face's transformers library) to summarize text.
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Reading comprehension: deploy an AllenNLP model for reading comprehension.
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Language identification: deploy a fastText model to identify languages.
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Multi-model text analysis: deploy 2 models (Sentiment and Summarization analyzers) in a single API.
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Image classification: deploy an AlexNet model from TorchVision to classify images.
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Image classification: deploy a ResNet50 model from TorchVision to classify images.
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Object detection: deploy a Faster R-CNN model from TorchVision to detect objects in images.
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Iris classification: deploy an XGBoost model (exported in ONNX) to classify iris flowers.
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YOLOv5 YouTube detection: deploy a YOLOv5 model trained on COCO val2017 dataset.
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Multi-model classification: deploy 3 models (ResNet50, MobileNet, ShuffleNet) in a single API.
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Iris classification: deploy a model to classify iris flowers.
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MPG estimation: deploy a linear regression model to estimate MPG.
- Entity recognizer: deploy a spacy model for named entity recognition.