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Google Cloud Machine Learning Platform

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Google Cloud Platform Fundamentals: Core Infrastructure

4.7 (14,949 ratings) | 150K Students Enrolled

Course 1 of 4 in the Architecting with Google Kubernetes Engine Specialization

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. Note: •Google services are currently unavailable in China.

Skills You'll Learn

Google Compute Engine, Google App Engine (GAE), Google Cloud Platform, Cloud Computing

Reviews

4.7 (14,949 ratings)

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SV

Feb 15, 2018

This was one of the best learning I had. Very simple and easy way to do the labs and understand the concepts. Even though the content is around fundamentals, it helps to remember the terminologies.

MS

Jan 06, 2019

Great introductions course. Very nice instructors with great charisma and skills. Useful labs, very thoughtful. I definitely recommend this course everyone who is willing to get started with GCP!

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

Taught By

Google Cloud Training

Transcript

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 a 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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