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Description
Model Garden serves as a catalog for AI and machine-learning models within the Google Cloud AI platform. It assists teams in discovering, assessing, tailoring, and implementing Google models alongside selected offerings from partners and open-source creators. Each model page offers tailored guidance and deployment alternatives; based on the specific model, users may have the opportunity to test, use a managed API, adjust settings, or deploy the model to a cloud endpoint. Developers can navigate through the Google Cloud console, utilizing supported APIs, SDKs, and command-line tools for their tasks. This resource is specifically designed for developers, data scientists, and machine-learning teams focused on creating generative AI and various applications powered by models. The availability of models, choices for deployment, and related usage costs can differ based on the model and its configuration. Therefore, users should review the specifics of each model to ensure optimal implementation.
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
Yes
API Access
Has API
No
Integrations
APERIO DataWise
No
Azure Marketplace
No
Camunda
No
Civo
No
Comet LLM
No
D2iQ
No
DagsHub
No
Gemini Enterprise Agent Platform
Yes
Gemini Enterprise Agent Platform Notebooks
No
Giskard
No
Integrations
APERIO DataWise
Yes
Azure Marketplace
Yes
Camunda
Yes
Civo
Yes
Comet LLM
Yes
D2iQ
Yes
DagsHub
Yes
Gemini Enterprise Agent Platform
No
Gemini Enterprise Agent Platform Notebooks
Yes
Giskard
Yes
Pricing Details
Costs depend on the selected model and its usage, plus any managed endpoint or compute resources used; applicable model and infrastructure rates vary by configuration.
Free Trial
No
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
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Founded
1998
Country
United States
Website
cloud.google.com/model-garden
Vendor Details
Company Name
Kubeflow
Website
www.kubeflow.org
Product Features
Product Features
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No
