What percent of system code does the ML model account for?
この演習はコースの一部です
理論を実践に変える、インタラクティブな演習のひとつをお試しください
This module previews the topics covered in the course and how to use Qwiklabs to complete each of your labs using Google Cloud.
This module explores what else a production ML system needs to do and how to meet those needs. You review how to make important, high-level, design decisions around training and model serving need to make in order to get the right performance profile for your model.
現在の演習
In this module, you learn how to recognize the ways that our model is dependent on our data, make cost-conscious engineering decisions, know when to roll back our models to earlier versions, debug the causes of observed model behavior and implement a pipeline that is immune to one type of dependency.
In this module, you identify performance considerations for machine learning models.Machine learning models are not all identical. For some models, you focus on improving I/O performance, and on others, you focus on squeezing out more computational speed.
Understand the tools and systems available and when to leverage hybrid machine learning models.
PDF links to all modules
