Google Cloud
Professional Machine Learning Engineer
Serving and monitoring ML on Google Cloud
Professional Machine Learning Engineer overlaps ModelOps on serving, pipeline automation, and monitoring, but also covers how the models themselves get built.
Who it suits
Fits practitioners responsible for getting models into production on a cloud, who want a recognized provider credential rather than a SAS-specific one.
Key differences
- It combines model development with productionization instead of focusing on registration, governance, and lifecycle management of existing models.
- Automation is expressed through cloud pipeline services rather than a vendor's model management tooling.
- Collaboration between data science and operations is implicit, while the SAS exam names operational collaboration explicitly.
- Level
- Professional
- Assessment
- Written
- Study time
- 90-150h
- Listed from
- $200
