Google Cloud Professional Machine Learning Engineer Cert Prep: 2 Architecting ML Solution

Google Cloud Professional Machine Learning Engineer Cert Prep: 2 Architecting ML Solution

1h 28mAdvanced2023-06-09

Authors

Noah Gift

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.

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
40,000 Toman