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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.

Description

Select the necessary configuration and resources for particular code segments in your ongoing project, as it only takes a few seconds to implement changes in a training scenario and secure the results. Opt for the appropriate setup for computational resources to initiate model training in mere seconds, allowing everything to be generated automatically without the hassle of infrastructure management. You can choose between serverless or dedicated operating modes, and efficiently manage project data, saving it to datasets while establishing connections to databases, object storage, or other repositories, all from a single interface. Collaborate with teammates globally to develop a machine learning model, share the project, and allocate budgets for teams throughout your organization. Launch your machine learning initiatives in minutes without requiring developer assistance, and conduct experiments that enable the simultaneous release of various model versions. This streamlined approach fosters innovation and enhances collaboration among team members, ensuring that everyone is on the same page.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

APERIO DataWise
Camunda
Comet LLM
D2iQ
DagsHub
Flyte
Giskard
Google Cloud Vertex AI Workbench
Jupyter Notebook
KServe
Kedro
PredictKube
PyTorch
TensorFlow
Union Cloud
Unremot
Yandex Cloud
Yandex Data Proc
YandexGPT
ZenML

Integrations

APERIO DataWise
Camunda
Comet LLM
D2iQ
DagsHub
Flyte
Giskard
Google Cloud Vertex AI Workbench
Jupyter Notebook
KServe
Kedro
PredictKube
PyTorch
TensorFlow
Union Cloud
Unremot
Yandex Cloud
Yandex Data Proc
YandexGPT
ZenML

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

$0.095437 per GB
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

Kubeflow

Website

www.kubeflow.org

Vendor Details

Company Name

Yandex.Cloud

Founded

1997

Country

Russia

Website

cloud.yandex.com/en/services/datasphere

Product Features

Machine Learning

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

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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