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

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

51mAdvanced2023-06-20

Authors

Noah Gift

Noah Gift

MLOps Expert | Solopreneur | Author | Adjunct Professor | CTO

Course details

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.

Skills covered

Google CloudMachine LearningSoftware Development ToolsGoogleCloud PlatformsCert PrepArtificial Intelligence (AI)Cloud ComputingSoftware Development

Concepts

0. Introduction

  • 01 - Overview
  • 02 - Course five key terminology

1. Designing and Implementing Training Pipelines

  • 03 - Prompt engineering for Google BigQuery with ChatGPT4
  • 04 - Getting started with Vertex AI
  • 05 - Understanding TPUs
  • 06 - TPUs as technology transition
  • 07 - Demo - TPU PyTorch MNIST

2. Implementing Serving Pipelines

  • 08 - TensorFlow serving with GPU-enabled Docker
  • 09 - Rust PyTorch microservice walkthrough
  • 10 - Demo - Rust pre-trained PyTorch microservice

Conclusion

  • 11 - Next steps

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