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

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Noah Gift

1:27:46

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01 - Introduction

01 - Overview.mp4

02:06

02 - Course 2 key terminology.mp4

02:47

03 - Cloud developer workspace advantage.mp4

04:10

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

01 - What is continuous delivery.mp4

02:50

02 - Containerized ML microservices.mp4

02:39

03 - SRE mindset for MLOps.mp4

27:30

04 - Reproducible workflow.mp4

01:40

05 - Learn continuous integration.mp4

32:14

03 - 2. Choosing Appropriate Google Cloud Hardware Components

01 - Selecting heavy vs. light MLOps.mp4

03:16

02 - Key components of MLOps landscape.mp4

03:43

03 - Feature store vs. data warehouse.mp4

02:00

04 - Compute choice.mp4

01:13

04 - Conclusion

01 - Next steps.mp4

01:38

Description

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.

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Machine Learning

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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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.