Coursera Bets $100 Million That Andrew Ng Can Do What Coursera Can’t

Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

DataCamp

Production Machine Learning Systems

via DataCamp

Go to class Write review

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.

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. This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.

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
    • PDF links to all modules
  • Course Resources
    • PDF links to all modules

Taught by

Google Cloud

Reviews

Start your review of Production Machine Learning Systems

Go to class

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free
Someone learning on their laptop while sitting on the floor.