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Production Machine Learning Systems

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Free Online Course: Production Machine Learning Systems provided by Coursera is a comprehensive online course, which lasts for 3 weeks long, 21 hours worth of material. The course is taught in English and is free of charge. Upon completion of the course, you can receive an e-certificate from Coursera. Production Machine Learning Systems is taught by Google Cloud Training.

Google Cloud via Coursera
3 weeks long, 21 hours worth of material
In progress
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.

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4.6, based on 924 Course reviews at Coursera

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