To support our site, Class Central may be compensated by some course providers.

Production Machine Learning Systems

Google Cloud and Google via Coursera

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

Taken this course? Share your experience with other students. Write review

Overview

Sign up to Coursera courses for free Learn how

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

Syllabus

WEEK 1

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.

 

WEEK 2

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

Help Center

Most commonly asked questions about Coursera Coursera

Reviews for Coursera's Production Machine Learning Systems
Based on 0 reviews

  • 5 star 0%
  • 4 star 0%
  • 3 star 0%
  • 2 star 0%
  • 1 star 0%

Did you take this course? Share your experience with other students.

Write a review

Class Central

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

Sign up for free

Never stop learning Never Stop Learning!

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