Google Cloud Machine Learning Platform

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Google Cloud Platform Fundamentals: Core Infrastructure

10984 ratings

Google Cloud

Google Cloud Platform Fundamentals: Core Infrastructure

10984 ratings

Course 1 of 3 in the Specialization Security in Google Cloud Platform

***NEW! Specialization Completion Challenge, receive Qwiklabs credits valued up to $180! See below for details.*** This course introduces you to important concepts and terminology for working with Google Cloud Platform (GCP). You learn about, and compare, many of the computing and storage services available in Google Cloud Platform, including Google App Engine, Google Compute Engine, Google Kubernetes Engine, Google Cloud Storage, Google Cloud SQL, and BigQuery. You learn about important resource and policy management tools, such as the Google Cloud Resource Manager hierarchy and Google Cloud Identity and Access Management. Hands-on labs give you foundational skills for working with GCP. SPECIALIZATION COMPLETION CHALLENGE As if learning new skills wasn’t enough of an incentive, we're excited to announce a special completion challenge for the 'Infrastructure' specializations. 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 $180 value given there are 6 courses in the Architecting with GCP 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! Note: •Google services are currently unavailable in China.

From the lesson

Big Data and Machine Learning in the Cloud

GCP's big-data and machine learning offerings are intended to help customers get the most out of data. These tools are intended to be simple and practical to embed in your applications. This module describes the available big-data and machine learning services and explains the usefulness of each.

Google Cloud Machine Learning Platform3:56

Machine learning APIs2:33

Meet the Instructors

Google Cloud Training

Machine learning is one branch of the field of artificial intelligence.

It's a way of solving problems without explicitly coding the solution.

Instead, human coders build systems that improve themselves over

time through repeated exposure to sample data which we call training data.

Major Google applications use machine learning like YouTube,

photos, the Google mobile App and Google translate.

The Google Machine Learning Platform is now available as a cloud service so

that you can add innovative capabilities to your own applications.

Cloud Machine Learning Platform provides modern machine learning services

with pre-trained models and a platform to generate your own tailored models.

As with other GCP products,

there's a range of services that stretches from the highly general to the pre-customized.

TensorFlow is an open source software library that's

exceptionally well suited for machine learning applications like neural networks.

It was developed by Google Brain for

Google's internal use and then open source so that the world could benefit.

You can run TensorFlow wherever you like but GCP is an ideal place for it because

machine learning models need lots of

on-demand compute resources and lots of training data.

TensorFlow can also take advantage of

Tensor processing units which are hardware devices designed to accelerate,

machine learning workloads with TensorFlow.

GCP makes them available in the cloud with compute engine virtual machines.

Each cloud TPU provides up to 180 teraflops of performance.

And because you pay for only what you use,

there's no upfront capital investment required.

Suppose you want a more managed service.

Google Cloud Machine Learning Engine lets you easily build

machine learning models that work on any type of data of any size.

It can take any TensorFlow model and perform large scale training on a managed cluster.

Finally, suppose you want to add various machine learning capabilities

to your applications without having to worry about the details of how they are provided.

Google Cloud also offers a range of machine learning APIs suited to specific purposes.

And I'll discuss them in a moment.

People use the Cloud Machine Learning Platform for lots of applications.

Generally, they fall into two categories depending

on whether the data they work on is structured or unstructured.

Based on structured data,

you can use ML for various kinds of

classification and regression tasks like customer churn analysis,

product diagnostics and forecasting.

It can be the heart of our recommendation engine for

content personalization and cross-sells and up-sells.

You can use ML to detect anomalies as in fraud detection,

sensor diagnostics or log metrics.

Based on unstructured data,

you can use ML for image analytics such as identifying damaged shipment,

identifying styles and flagging content.

You can do text analytics too.

Like a call center, blog analysis,

language identification, topic classification and sentiment analysis.

In many of the most innovative applications for machine learning,

several of these kinds of applications are combined.

What if whenever one of your customers

posted praise for one of your products on social media,

your application could automatically reach out to them

with a customized discount on another product they'll probably like.

The Google Cloud machine Learning Platform makes

that kind of interactivity well within your grasp.

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