Google Cloud Professional Machine Learning Engineer Cert Prep: 6 Monitoring, Optimizing, and Maintaining ML Solutions

سرفصل های دوره

The Google Professional Machine Learning Engineer certification lets prospective employers know that you have the knowledge 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 sixth and final course in the series, Noah Gift covers monitoring, optimizing, and maintaining ML solutions. Noah starts with the key topic of data drift and its impact on model performance. He provides demos to ML solutions like auditing, logging, cloud web security scanners, and more. Noah also explains the “five whys” method of problem solving and why it’s an effective approach to debugging.

01 - Introduction

01 - Overview

02 - Course six key terminology

02 - 1. ML Solutions

01 - Data drift explained by naughty child problem

02 - Load testing with Locust

03 - Demo Auditing via logs

04 - Demo Logging dashboard

05 - Demo Cloud web security scanner

06 - Demo Querying logging output with BigQuery

07 - Demo Load testing with Rust

08 - Five whys

09 - Using Google Courses

10 - Building Rust HuggingFace Translator

11 - Using PyTorch Rust stable diffusion

12 - Using Rust with PyTorch

13 - Building a CUDA GPU stress test

03 - Conclusion

01 - Next steps

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تولید کننده: LinkedIn Learning (Lynda)