Google Cloud Professional Machine Learning Engineer Cert Prep: 2 Architecting ML Solution
1h 28mAdvanced2023-06-09
Authors
Noah Gift
MLOps Expert | Solopreneur | Author | Adjunct Professor | CTO
Course details
Earning a Google Professional Machine Learning Engineer certification demonstrates your ability to design, build, and productionize machine learning models to solve business challenges using Google Cloud technologies, and knowledge of proven ML models and techniques.
In this second course in the certification prep series, instructor Noah Gift covers topics relating to architecting machine learning solutions. He shows you how to design reliable, scalable, and highly-available ML solutions, covering topics like continuous delivery, reproducible workflow, and continuous integration. Noah then explains how to choose the appropriate Google Cloud hardware components for your ML solutions.
In this second course in the certification prep series, instructor Noah Gift covers topics relating to architecting machine learning solutions. He shows you how to design reliable, scalable, and highly-available ML solutions, covering topics like continuous delivery, reproducible workflow, and continuous integration. Noah then explains how to choose the appropriate Google Cloud hardware components for your ML solutions.
Skills covered
Google CloudSoftware ArchitectureMachine LearningGoogleSoftware Development ToolsCloud PlatformsArtificial Intelligence (AI)Cert PrepCloud ComputingSoftware Development
Concepts
Introduction
- Overview
- Course 2 key terminology
- Cloud developer workspace advantage
Designing a Reliable, Scalable, and Highly Available ML Solution
- What is continuous delivery
- Containerized ML microservices
- SRE mindset for MLOps
- Reproducible workflow
- Learn continuous integration
Choosing Appropriate Google Cloud Hardware Components
- Selecting heavy vs. light MLOps
- Key components of MLOps landscape
- Feature store vs. data warehouse
- Compute choice
Conclusion
- Next steps