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Average Ratings 0 Ratings
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
Red Hat Advanced Cluster Management for Kubernetes allows users to oversee clusters and applications through a centralized interface, complete with integrated security policies. By enhancing the capabilities of Red Hat OpenShift, it facilitates the deployment of applications, the management of multiple clusters, and the implementation of policies across numerous clusters at scale. This solution guarantees compliance, tracks usage, and maintains uniformity across deployments. Included with Red Hat OpenShift Platform Plus, it provides an extensive array of powerful tools designed to secure, protect, and manage applications effectively. Users can operate from any environment where Red Hat OpenShift is available and can manage any Kubernetes cluster within their ecosystem. The self-service provisioning feature accelerates application development pipelines, enabling swift deployment of both legacy and cloud-native applications across various distributed clusters. Additionally, self-service cluster deployment empowers IT departments by automating the application delivery process, allowing them to focus on higher-level strategic initiatives. As a result, organizations can achieve greater efficiency and agility in their IT operations.
API Access
Has API
Yes
API Access
Has API
No
Integrations
Kubernetes
Yes
Red Hat OpenShift
Yes
Amazon Web Services (AWS)
No
Ansible
No
IBM Cloud
No
Microsoft Azure
No
PyTorch
Yes
TensorFlow
Yes
Integrations
Kubernetes
Yes
Red Hat OpenShift
Yes
Amazon Web Services (AWS)
Yes
Ansible
Yes
IBM Cloud
Yes
Microsoft Azure
Yes
PyTorch
No
TensorFlow
No
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
No
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
IBM
Country
United States
Website
developer.ibm.com/apis/catalog/edgeai--distributed-ai-apis/Introduction/
Vendor Details
Company Name
Red Hat
Founded
1993
Country
United States
Website
www.redhat.com/en/technologies/management/advanced-cluster-management