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What is IBM Storage for Red Hat OpenShift?

IBM Storage for Red Hat OpenShift offers a smooth integration of traditional and container storage, making it easy to implement scalable microservices architectures suitable for enterprises. This solution has been tested in conjunction with Red Hat OpenShift, Kubernetes, and IBM Cloud Pak, which guarantees an efficient deployment and management experience. It features advanced data protection, automated scheduling, and capabilities for data reuse that are specifically designed for environments using Red Hat OpenShift and Kubernetes. Users can quickly deploy the necessary resources thanks to its support for block, file, and object data types. Moreover, IBM Storage for Red Hat OpenShift establishes a solid and flexible hybrid cloud infrastructure on-premises, delivering essential storage orchestration and infrastructure. In addition, the platform enhances container efficiency in Kubernetes settings by incorporating Container Storage Interface (CSI) support for both block and file storage options. This all-encompassing strategy equips organizations with the tools to refine their storage methodologies, driving both efficiency and scalability to new heights. Organizations can thus confidently embrace innovation while managing their data more effectively.

What is IBM Distributed AI APIs?

Distributed AI is a computing methodology that allows for data analysis to occur right where the data resides, thereby avoiding the need for transferring extensive data sets. Originating from IBM Research, the Distributed AI APIs provide a collection of RESTful web services that include data and artificial intelligence algorithms specifically designed for use in hybrid cloud, edge computing, and distributed environments. Each API within this framework is crafted to address the specific challenges encountered while implementing AI technologies in these varied settings. Importantly, these APIs do not focus on the foundational elements of developing and executing AI workflows, such as the training or serving of models. Instead, developers have the flexibility to employ their preferred open-source libraries, like TensorFlow or PyTorch, for those functions. Once the application is developed, it can be encapsulated with the complete AI pipeline into containers, ready for deployment across different distributed locations. Furthermore, utilizing container orchestration platforms such as Kubernetes or OpenShift significantly enhances the automation of the deployment process, ensuring that distributed AI applications are managed with both efficiency and scalability. This cutting-edge methodology not only simplifies the integration of AI within various infrastructures but also promotes the development of more intelligent and responsive solutions across numerous industries. Ultimately, it paves the way for a future where AI is seamlessly embedded into the fabric of technology.

Media

Media

Integrations Supported

Kubernetes
PyTorch
Red Hat OpenShift
TensorFlow

Integrations Supported

Kubernetes
PyTorch
Red Hat OpenShift
TensorFlow

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

IBM

Date Founded

1911

Company Location

United States

Company Website

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

Company Facts

Organization Name

IBM

Company Location

United States

Company Website

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

Categories and 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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