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Production Machine Learning Systems

Google Cloud and Google via Coursera

0 Reviews 101 students interested
  • Provider Coursera
  • Subject Machine Learning
  • Cost $49
  • Session In progress
  • Language English
  • Certificate Paid Certificate Available
  • Effort 5-7 hours a week
  • Start Date
  • Duration 2 weeks long
  • Learn more about MOOCs

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***NEW! Specialization Completion Challenge, receive Qwiklabs credits valued up to $150! See below for details.***

In the second course of this specialization, we will dive into the components and best practices of a high-performing ML system in production environments.

Prerequisites: Basic SQL, familiarity with Python and TensorFlow

As if learning new skills wasn’t enough of an incentive, we're excited to announce a special completion challenge for 'Advanced Machine Learning with TensorFlow on GCP’ specialization.

Here’s how it works: Our completion challenge runs through 11:59pm PT May 5, 2019. Complete any course in this Specialization including this one, anytime in this period and we'll send you 30 Qwiklabs credits for each course completed (upto $150 value given there are 5 courses in the specialization).

You can use these credits to take additional labs and earn badges, which you can then add to your resume and social profiles.

Your challenge awaits – begin learning on Coursera today!



Welcome to the course

In this module we will preview the topics covered in the course and how to use Qwiklabs to complete each of your labs using Google Cloud Platform.


Architecting Production ML Systems

In this module, we’ll talk about what else a production ML system needs to do and how you can meet those needs. We’ll then review some important, high-level, design decisions around training and model serving that you’ll need to make in order to get the right performance profile for your model.


Ingesting data for Cloud-based analytics and ML

In this module, we’ll talk about how to bring your data to the cloud. There are many ways to bring your data into cloud to power your machine learning models. We’ll first review why your data needs to be on the cloud to get the advantages of scale and using fully-managed services and what options you have to bring your data over.



Designing Adaptable ML systems

In this module, we’ll 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 will learn how to identify performance considerations for machine learning models. Machine learning models are not all identical. For some models, you will be focused on improving I/O performance, and on others, you will be focused on squeezing out more computational speed.


Hybrid ML systems

Understand the tools and systems available and when to leverage hybrid machine learning models.


Course Summary

Review the content covered in the modules on Production ML systems


Taught by

Google Cloud Training

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