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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

The Kubeflow initiative aims to simplify the process of deploying machine learning workflows on Kubernetes, ensuring they are both portable and scalable. Rather than duplicating existing services, our focus is on offering an easy-to-use platform for implementing top-tier open-source ML systems across various infrastructures. Kubeflow is designed to operate seamlessly wherever Kubernetes is running. It features a specialized TensorFlow training job operator that facilitates the training of machine learning models, particularly excelling in managing distributed TensorFlow training tasks. Users can fine-tune the training controller to utilize either CPUs or GPUs, adapting it to different cluster configurations. In addition, Kubeflow provides functionalities to create and oversee interactive Jupyter notebooks, allowing for tailored deployments and resource allocation specific to data science tasks. You can test and refine your workflows locally before transitioning them to a cloud environment whenever you are prepared. This flexibility empowers data scientists to iterate efficiently, ensuring that their models are robust and ready for production.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Kubernetes
APERIO DataWise
Azure Marketplace
Camunda
Civo
Comet LLM
D2iQ
DagsHub
Flyte
Giskard
KServe
PredictKube
PyTorch
Red Hat OpenShift
Robust Intelligence
Superwise
TensorFlow
Union Cloud
Unremot
ZenML

Integrations

Kubernetes
APERIO DataWise
Azure Marketplace
Camunda
Civo
Comet LLM
D2iQ
DagsHub
Flyte
Giskard
KServe
PredictKube
PyTorch
Red Hat OpenShift
Robust Intelligence
Superwise
TensorFlow
Union Cloud
Unremot
ZenML

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

Kubeflow

Website

www.kubeflow.org

Product Features

Machine Learning

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

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