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Google Cloud

Production Machine Learning Systems

Google Cloud and Google via Coursera

Overview

This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.

Syllabus

  • Introduction to Advanced Machine Learning on Google Cloud
    • This module previews the topics covered in the course and how to use Qwiklabs to complete each of your labs using Google Cloud.
  • Architecting Production ML Systems
    • 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.
  • Designing Adaptable ML Systems
    • 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.
  • Designing High-Performance ML Systems
    • 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.
  • Building Hybrid ML Systems
    • Understand the tools and systems available and when to leverage hybrid machine learning models.
  • Summary
    • This module reviews what you learned in this course.

Taught by

Google Cloud Training

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Reviews

1.0 rating, based on 1 reviews

Start your review of Production Machine Learning Systems

  • Profile image for Jakub Bartczuk
    Jakub Bartczuk

    Jakub Bartczuk completed this course.

    The video content is ok, but the assignments and quizzes are abysmal - they don't really test anything, labs have some sections that don't work, and the supposed TODO sections don't exist.

    I definitely don't recommend this course if you're not going to apply this stuff immediately (so you can actually go through lab resources on your own data)

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