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

سرفصل های دوره

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.

01 - Introduction

01 - Overview

02 - Course 2 key terminology

03 - Cloud developer workspace advantage

02 - 1. Designing a Reliable, Scalable, and Highly Available ML Solution

01 - What is continuous delivery

02 - Containerized ML microservices

03 - SRE mindset for MLOps

04 - Reproducible workflow

05 - Learn continuous integration

03 - 2. Choosing Appropriate Google Cloud Hardware Components

01 - Selecting heavy vs. light MLOps

02 - Key components of MLOps landscape

03 - Feature store vs. data warehouse

04 - Compute choice

04 - Conclusion

01 - Next steps

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تولید کننده: LinkedIn Learning (Lynda)