Google Cloud Professional Machine Learning Engineer Cert Prep: 5 Automating and Orchestrating ML Pipelines

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Noah Gift

51:19

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01 - Introduction

01 - Overview.mp4

01:22

02 - Course five key terminology.mp4

03:23

02 - 1. Designing and Implementing Training Pipelines

01 - Prompt engineering for Google BigQuery with ChatGPT4.mp4

09:20

02 - Getting started with Vertex AI.mp4

02:30

03 - Understanding TPUs.mp4

05:10

04 - TPUs as technology transition.mp4

03:54

05 - Demo TPU PyTorch MNIST.mp4

04:14

03 - 2. Implementing Serving Pipelines

01 - TensorFlow serving with GPU-enabled Docker.mp4

05:59

02 - Rust PyTorch microservice walkthrough.mp4

07:03

03 - Demo Rust pre-trained PyTorch microservice.mp4

06:41

04 - Conclusion

01 - Next steps.mp4

01:43

Description

Earning the 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 fifth course in the certification prep series, instructor Noah Gift covers core concepts relating to automating and orchestrating ML pipelines. Noah explains how to design and implement training pipelines, including how to engineer prompts for Google BigQuery with ChatGPT4. Then, learn about implementing serving pipelines, as Noah explains some of the characteristics of GPU-enabled Docker containers, gives a Rust PyTorch microservice walkthrough, and demos a Rust pre-trained PyTorch microservice.

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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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