Editions
TigerGraph ML Workbench is available as a service through TigerGraph Cloud or standalone in two editions, Developer and Enterprise.
While both on-prem editions offer the same useful Python-level features for your data science needs, the Enterprise edition offers more powerful features to support production-level model training on enterprise-level data sets.
The tables below can help you compare the key differences between the editions.
ML Workbench on TigerGraph Cloud
ML Workbench on TigerGraph Cloud is available through a Cloud Starter Kit selected when you provision a new Cloud instance. This is a direct link to a ML Workbench instance with a Jupyter Notebook introduction for you to begin with the tutorials or with your own data.
Developer and Enterprise On-Prem Editions
Features
Developer | Enterprise | |
---|---|---|
Compatibility |
|
|
Onboarding |
|
|
Capabilities |
Python-level capabilities with pyTigerGraph:
|
Python-level capabilities with pyTigerGraph:
|
Data Export Method |
HTTP only |
Reliable and efficient data export via both HTTP and Kafka |
Data Export Size |
Limited to 2GB |
Unlimited |
Parallel Training |
No |
Yes |
Support |
Community support |
|
Customer Scenarios
Developer Edition | Enterprise Edition | |
---|---|---|
Purpose |
|
Production deployment of Graph ML Models |
Audience |
|
Enterprise data science teams |
Infrastructure Readiness |
Local machines / Internal ML infra (self-managed) |
Local machines / Internal ML Infra (self-managed) |
Data Size |
Small data set (<2GB) |
Production / Enterprise level data |
Performance requirements |
|
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