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

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Noah Gift

1:10:31

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01 - Introduction

01 - Overview.mp4

01:23

02 - Course six key terminology.mp4

03:14

02 - 1. ML Solutions

01 - Data drift explained by naughty child problem.mp4

01:39

02 - Load testing with Locust.mp4

03:25

03 - Demo Auditing via logs.mp4

02:20

04 - Demo Logging dashboard.mp4

03:41

05 - Demo Cloud web security scanner.mp4

02:46

06 - Demo Querying logging output with BigQuery.mp4

03:49

07 - Demo Load testing with Rust.mp4

05:41

08 - Five whys.mp4

04:06

09 - Using Google Courses.mp4

03:11

10 - Building Rust HuggingFace Translator.mp4

06:14

11 - Using PyTorch Rust stable diffusion.mp4

07:04

12 - Using Rust with PyTorch.mp4

07:52

13 - Building a CUDA GPU stress test.mp4

07:38

03 - Conclusion

01 - Next steps.mp4

06:28

Description

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.

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Machine Learning

Cloud Computing

Google Cloud

Noah Gift

Instructor's Courses

Author of Practical MLOps, Enterprise MLOps, Developing on AWS with C#, Pragmatic AI, and Python for DevOps. Certified on Multiple MLOps certifications including Google-Professional Machine Learning Engineer and AWS Certified Machine Learning - Specialty. Adjunct Professor at Duke MIDS & Northwestern Graduate Data Science & AI. Held business roles including CTO, general manager, consulting CTO, and cloud architect. Consults with start-ups and other companies on machine learning and cloud architecture. AWS ML Hero, Python Software Foundation Fellow, AWS Subject Matter on Machine Learning, AWS Certified Solutions Architect, AWS Certified Machine Learning Specialist, AWS Certified Big Data Specialist, Google Certified Professional Architect, and AWS Academy Accredited Instructor. Published books, videos, and courses on cloud machine learning, DevOps, Python, Data Science, Big Data and AI. ★ Specialties ★ ° Cloud-native Machine Learning and AI ° Directly teaching cutting edge skills to students that lead to jobs ° Creating world-class content in all forms ° Building Companies ° Shipping new Products ° Leading and growing engineering teams ° Production Machine Learning, Deep Learning, Big Data, and AI ° Serverless Data Engineering ° Advising Early Stage Startups/Consulting CTO services ° Distributed Systems and Scalability

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