Pre-trained machine learning models available in AWS Marketplace
Build AI and ML powered solutions faster.
What are pre-trained machine learning models?
Pre-trained Machine Learning (ML) models are ready-to-use models that can be quickly deployed on Amazon SageMaker, a fully managed cloud machine learning platform. By pre-training the ML models for you, solutions in AWS Marketplace take care of the heavy lifting, helping you deliver AI and ML powered features faster and at a lower cost.
Benefits of using pre-trained models in AWS Marketplace
Faster AI/ML solution development
Enhanced security & data privacy
▶ Scanned for security vulnerabilities, pre-trained models in AWS Marketplace help you gain control over your data and enhance security.
▶ Models are always deployed in network isolation mode with an option to deploy them in your Amazon Virtual Private Cloud (VPC) to implement additional security features that restrict and monitor traffic.
Flexible evaluation & pricing options
▶ Pricing options like pay-as-you-go and annual contracts via Private Offers are available, giving you the flexibility you need regardless of the duration of use.
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How to deploy pre-trained models in Amazon SageMaker
Pre-trained models in AWS Marketplace can be deployed directly on Amazon SageMaker through a Jupyter Notebook, SageMaker SDK, or AWS CLI. This experience is powered by the AWS Marketplace catalog and APIs so you get all of the same benefits such as seamless deployment and simplified billing.
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Featured Products
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Social distancing detector
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Mask detector for epidemiological safety
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Hard hat detector
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Social distancing detector
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Image classification model that analyzes social distancing in public areas
Provectus’ Social Distancing Detector analyzes the distances between people based on their approximate height. This solution fits well into video analytic workloads for businesses that are looking to minimize disease transmission risks, gather data for decision-making processes, and ensure adequate personal space for their employees and clients.
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Mask detector for epidemiological safety
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Image recognition model to detect the absence of masks or respirators
ViTech Lab’s Mask Detector for Epidemiological Safety improves safety in laboratories, healthcare facilities, educational institutions, industrial companies, military facilities and more. It is trained on a manually selected dataset and works with live footage from CCTV cameras. ViTech Lab’s Mask Detector for Epidemiological Safety detects mask and automatically notifies safety engineers of non-compliance.
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Hard hat detector
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Real-time detection of PPE compliance for factory floor settings and construction sites
Prevectus’ Hard Hat Detector for Industrial Worker Safety is a computer vision-driven ML model designed to detect PPE compliance on a factory floor or construction site. This solution analyzes images to determine if a hard hat is being worn in real-time. Trained on data from CCTV cameras, this ML model can be used in oil & gas, manufacturing, construction, and steelmaking industries to help ensure safety and compliance.
Object detection
State of the art open source computer vision models trained using ResNet50, YOLOv3, Faster-RCNN to detect thousands of common category objects.
Detect multiple objects on the input image including category names, confidence scores, and absolute locations.
Image feature extraction and ImageNet category prediction model for highly efficient image classification.
A powerful network for fast and accurate object detection, trained on COCO dataset with 80 common object categories.
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Purpose built CV models
Pre-trained computer vision models that help automate data extraction and gather insights.
Automatically detects a car license plate and identifies the license plate characters.
Extracts key entities from a passport page to automate form filling.
Vehicle detector model for traffic analytics and intelligent transportation systems that operates from aerial and elevated perspectives.
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Text/Natural Language Processing models
Pre-trained models for text/natural language processing tasks like named entity recognition (NER) and text summarization.
Text summarizer with the ability to reduce the size of long documents in to a few sentences.
Extracts entities such as brand, color, category from supplied text.
Detects 18 different entity types including buildings, works of art, landmarks, locations, and vehicles.
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How pre-trained models help solve real-world use cases
The AWS Marketplace hackathon took place in March 2020 and had thousand participants collaborating on 57 projects. Here are three stand out projects from the event that demonstrate how pre-trained models can be used to help solve real-world use cases.
A traffic monitoring and visualization platform that utilizes ML models to analyze mobility patterns.
A chat bot that utilizes ML models to support inferences and helps you at work, saving you time and boosting your productivity.
Camera-enabled IoT device powered by ML Models to detect if people are wearing masks when accessing restricted areas.
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Interested in learning more?
Here are some useful resources for you.