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Average Ratings 0 Ratings
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
Integrations
APERIO DataWise
Camunda
Comet LLM
D2iQ
DagsHub
Flyte
Giskard
Google Cloud Vertex AI Workbench
Jupyter Notebook
KServe
Integrations
APERIO DataWise
Camunda
Comet LLM
D2iQ
DagsHub
Flyte
Giskard
Google Cloud Vertex AI Workbench
Jupyter Notebook
KServe
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