Benefits of a Private SaaS Architecture
Self Hosted vs Private SaaS vs Public SaaS?
At the top level your team/project is making a decision about how they are going to build, expand, support, and maintain a machine learning pipeline.
Conceptual ML Pipeline
Concrete/Real World Example
When building out a framework to support ML Pipelines there are three main options:
- Self Hosted
- Private SaaS
- Public SaaS
The other choice, that we're not going to cover here, is whether you use AWS, Azure, GCP, or something else. Workbench is architected and powered by a broad and rich set of AWS ML Pipeline services. We believe that AWS provides the best set of functionality and APIs for flexible, real world ML architectures.
Resources
See our full presentation on the Workbench Private SaaS Architecture