This is a Text Summarization model built upon a Transformer model from [Hugging Face](https://huggingface.co/sshleifer/distilbart-cnn-12-6). The deployed model can be used for running inference on any English input text.
Model Package - Fulfilled on Amazon SageMaker
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Sensifai offers automatic face recognition and identification. For example, our basic software recognizes thousands of celebrities in videos. In Sagemaker platform, you can easily fine-tune this software to recognize a new set of people or celebrities and tag them in videos by providing the...
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The NavInfo Europe Generic Segmentation Model is a pre-trained semantic segmentation model trained on 7 classes suitable for autonomous driving, mapping and road asset management use-cases.
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This model identifies if a given parking slot is occupied or not. It is trained using convolutional neural network (CNN) on parking lot images to identify occupancy. This model can be extended as edge ML model with a parking monitoring drone that continuously monitors available and occupied parking...
Model Package - Fulfilled on Amazon SageMaker
CAD (Contextual Anomaly Detection) is a technology that monitors and learns normal patterns for time-series data with specific patterns and detects abnormal patterns that deviate from the normal pattern. Unlike Point Anomaly Detection, it can identify anomaly patterns in time-series data even if...
Algorithm - Fulfilled on Amazon SageMaker
Prosper Insights & Analytics' propensity model predicts the probability that a U.S. adult consumer shops at a specific retailer. Based on a set of basic demographics, the model identifies individuals who are likely to shop at that retailer. The model was trained with data from Prosper's large...
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Active Learning for Text Classification trains a text classification model using a small corpus of training data and provides the most appropriate samples from a huge corpus of unlabeled data to be annotated in order to improve the model accuracy significantly. Using Active Learning this algorithm...
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Random Forests for regression on sparse data set like LibSVM without translating the data set into other formats like recordIO. The algorithm scales efficiently across multi-cores on a single AWS EC2 Instance out of the box.
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This solution identifies bank customers who are more likely to sign up for a term deposit with the bank in response to the bank’s marketing campaign. The solution consists of a pretrained model that analyzes a combination of campaign features and customer characteristics to make predictions about...
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Using predictive modeling and machine learning techniques, CARE™ Disease Prediction analyzes more than 14 billion medical claims to identify early indicators of disease for local markets and disease onset across the United States. These predictions give you the information you need to plan outreach...
Model Package - Fulfilled on Amazon SageMaker
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