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Description
Effortlessly identify individuals and their postures straight away, with the ability to adjust settings for specific needs. You can also enhance functionality by incorporating your own AI models, whether utilizing TensorFlow Lite models sourced online or creating entirely new ones. This solution is also compatible with Google Coral, proving that developers can indeed have an aesthetic vision! The elegant design of Darcy Cam looks fantastic in both laboratory settings and everyday environments. Equipped with dual Coral AI accelerators and a video sensor, it supports multiple models for real-time efficiency. Establish various network configurations, including multi-cloud, hybrid cloud, and edge-to-edge setups, all with a single command and without the need for open ports, VPNs, or NAT tunnels. Manage all elements of your edge infrastructure directly from the Cloud, including tasks such as provisioning, deployment, orchestration, resource management, monitoring, and updates. Streamline operations by automating workflows integrated with your existing clouds, tools, and applications, while also ensuring seamless connectivity with your current infrastructure via OpenAPI. This comprehensive approach allows for maximum flexibility and efficiency in managing advanced AI capabilities.
Description
Distributed AI represents a computing approach that eliminates the necessity of transferring large data sets, enabling data analysis directly at its origin. Developed by IBM Research, the Distributed AI APIs consist of a suite of RESTful web services equipped with data and AI algorithms tailored for AI applications in hybrid cloud, edge, and distributed computing scenarios. Each API within the Distributed AI framework tackles the unique challenges associated with deploying AI technologies in such environments. Notably, these APIs do not concentrate on fundamental aspects of establishing and implementing AI workflows, such as model training or serving. Instead, developers can utilize their preferred open-source libraries like TensorFlow or PyTorch for these tasks. Afterward, you can encapsulate your application, which includes the entire AI pipeline, into containers for deployment at various distributed sites. Additionally, leveraging container orchestration tools like Kubernetes or OpenShift can greatly enhance the automation of the deployment process, ensuring efficiency and scalability in managing distributed AI applications. This innovative approach ultimately streamlines the integration of AI into diverse infrastructures, fostering smarter solutions.
API Access
Has API
API Access
Has API
Integrations
Kubernetes
PyTorch
Raspberry Pi OS
Red Hat OpenShift
TensorFlow
Integrations
Kubernetes
PyTorch
Raspberry Pi OS
Red Hat OpenShift
TensorFlow
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Edgeworx
Founded
2017
Country
United States
Website
www.darcy.ai/
Vendor Details
Company Name
IBM
Country
United States
Website
developer.ibm.com/apis/catalog/edgeai--distributed-ai-apis/Introduction/
Product Features
Artificial Intelligence
Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)