Skip to content

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

Data ⮕ Features ⮕ Models ⮕ Deployment (end-user application)

Concrete/Real World Example

S3 ⮕ Glue Job ⮕ Data Catalog ⮕ FeatureGroups ⮕ Models ⮕ Endpoints ⮕ App

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. SageWorks 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.

private_saas_compare

Resources

See our full presentation on the SageWorks Private SaaS Architecture