Google Cloud Professional Machine Learning Engineer Cert Prep: 4 Developing ML Models

Focused View

Noah Gift

1:29:44

191 View

01 - Introduction

01 - Overview.mp4

01:18

02 - Course four key terminology.mp4

03:37

02 - 1. Building Models

01 - Using TensorFlow Playground.mp4

02:01

02 - Overfitting vs. underfitting.mp4

01:58

03 - Selecting the right metrics.mp4

05:33

03 - 2. Training Models

01 - Training models with TensorFlow GPU-enabled Docker.mp4

03:54

02 - Fine-tuning raw ingredients Hugging Face.mp4

01:36

03 - Advantages transfer learning.mp4

02:08

04 - 3. Scaling Model Training and Serving

01 - Operationalize microservices.mp4

01:57

02 - Monitoring and logging with Rust on Google App Engine.mp4

03:35

03 - Continuous integration Rust with GitHub Actions.mp4

07:52

04 - Demo Unit testing Rust.mp4

06:36

05 - Demo GitHub copilot-enabled Rust.mp4

09:15

06 - Setup GCP workstation with Python.mp4

04:41

07 - Demo Google Cloud Shell.mp4

04:23

08 - Demo Google Cloud Editor.mp4

04:41

09 - Demo Google CLI SDK.mp4

06:18

10 - Demo Google gcloud CLI.mp4

06:14

11 - Demo Google App Engine Rust Deploy.mp4

05:15

12 - Demo Google App Engine Golang.mp4

05:08

05 - Conclusion

01 - Next steps.mp4

01:44

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 fourth course in the certification prep series, instructor Noah Gift covers topics relating to developing machine learning models. He shows you how to build models using TensorFlow, explains the concepts of overfitting versus underfitting, and how to select the right evaluation metrics. Noah then explains how to train modes, including the advantages of transfer learning. Finally, learn about scaling model training and serving, including how to build a microservice and deploy it with Rust on Google App Engine.

More details

User Reviews

Rating

average 0

Total votes0

Focused display

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

Linkedin Learning

View courses Linkedin Learning

LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.