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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.

Description

IBM Storage for Red Hat OpenShift seamlessly integrates traditional and container storage, facilitating the deployment of enterprise-grade scale-out microservices architectures with ease. This solution has been validated alongside Red Hat OpenShift, Kubernetes, and IBM Cloud Pak, ensuring a streamlined deployment and management process for a cohesive experience. It offers enterprise-level data protection, automated scheduling, and data reuse capabilities specifically tailored for Red Hat OpenShift and Kubernetes settings. With support for block, file, and object data resources, users can swiftly deploy their required resources as needed. Additionally, IBM Storage for Red Hat OpenShift lays the groundwork for a robust and agile hybrid cloud environment on-premises, providing the essential infrastructure and storage orchestration. Furthermore, IBM enhances container utilization in Kubernetes environments by supporting Container Storage Interface (CSI) for its block and file storage solutions. This comprehensive approach empowers organizations to optimize their storage strategies while maximizing efficiency and scalability.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Kubernetes
PyTorch
Red Hat OpenShift
TensorFlow

Integrations

Kubernetes
PyTorch
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

IBM

Country

United States

Website

developer.ibm.com/apis/catalog/edgeai--distributed-ai-apis/Introduction/

Vendor Details

Company Name

IBM

Founded

1911

Country

United States

Website

www.ibm.com/it-infrastructure/storage/containers/redhat-openshift

Product Features

Container Management

Access Control
Application Development
Automatic Scaling
Build Automation
Container Health Management
Container Storage
Deployment Automation
File Isolation
Hybrid Deployments
Network Isolation
Orchestration
Shared File Systems
Version Control
Virtualization

Container Security

Access Roles / Permissions
Application Performance Tracking
Centralized Policy Management
Container Stack Scanning
Image Vulnerability Detection
Reporting
Testing
View Container Metadata

Integration

Dashboard
ETL - Extract / Transform / Load
Metadata Management
Multiple Data Sources
Web Services

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