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What is Protégé?

Protégé is supported by a strong community of users spanning academia, industry, and government, who leverage the platform to develop knowledge-driven solutions in diverse sectors like biomedicine, e-commerce, and organizational modeling. Its flexible plug-in architecture facilitates the creation of both simple and complex ontology-based applications, allowing developers to easily integrate Protégé's outputs with rule systems and other problem-solving tools. This capability empowers the formation of a wide range of intelligent systems tailored to various needs. Importantly, the dedicated team at Stanford, along with the vibrant Protégé community, is available to offer support and guidance. Members of this community actively contribute by answering questions, enhancing documentation, and creating new plug-ins. Moreover, Protégé's Java foundations enhance its extensibility, while its plug-and-play configuration provides a versatile platform for rapid prototyping and application development. This functionality not only fosters innovation but also encourages collaboration among users to tackle complex challenges effectively. With its rich features and strong community backing, Protégé continues to be a valuable resource for knowledge representation and system development.

What is Google Deep Learning Containers?

Speed up the progress of your deep learning initiative on Google Cloud by leveraging Deep Learning Containers, which allow you to rapidly prototype within a consistent and dependable setting for your AI projects that includes development, testing, and deployment stages. These Docker images come pre-optimized for high performance, are rigorously validated for compatibility, and are ready for immediate use with widely-used frameworks. Utilizing Deep Learning Containers guarantees a unified environment across the diverse services provided by Google Cloud, making it easy to scale in the cloud or shift from local infrastructures. Moreover, you can deploy your applications on various platforms such as Google Kubernetes Engine (GKE), AI Platform, Cloud Run, Compute Engine, Kubernetes, and Docker Swarm, offering you a range of choices to align with your project's specific requirements. This level of adaptability not only boosts your operational efficiency but also allows for swift adjustments to evolving project demands, ensuring that you remain ahead in the dynamic landscape of deep learning. In summary, adopting Deep Learning Containers can significantly streamline your workflow and enhance your overall productivity.

Media

Media

Integrations Supported

Google Cloud Platform
Google Cloud Run
Google Compute Engine
Google Kubernetes Engine (GKE)
Kubernetes

Integrations Supported

Google Cloud Platform
Google Cloud Run
Google Compute Engine
Google Kubernetes Engine (GKE)
Kubernetes

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Center for Biomedical Informatics Research

Company Location

United States

Company Website

protege.stanford.edu/

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

cloud.google.com/ai-platform/deep-learning-containers

Categories and Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Categories and Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

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