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

سرفصل های دوره

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.

01 - Introduction

01 - Overview

02 - Course five key terminology

02 - 1. Designing and Implementing Training Pipelines

01 - Prompt engineering for Google BigQuery with ChatGPT4

02 - Getting started with Vertex AI

03 - Understanding TPUs

04 - TPUs as technology transition

05 - Demo TPU PyTorch MNIST

03 - 2. Implementing Serving Pipelines

01 - TensorFlow serving with GPU-enabled Docker

02 - Rust PyTorch microservice walkthrough

03 - Demo Rust pre-trained PyTorch microservice

04 - Conclusion

01 - Next steps

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تولید کننده: LinkedIn Learning (Lynda)