Google Cloud Machine Learning ( ML ) Tutorial

Google Cloud Machine Learning ( ML ) Tutorial
19th Sep, 2019
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Google Cloud Machine Learning (ML)

Let’s start this Google Cloud Machine learning tutorial by understanding how MLaaS impact businesses today and where does Google Cloud fit in. We can see a huge shift in the way businesses are getting involved with their technologies and software.  The said shift is driven by the advent and acceptance of Cloud computing. Besides of all the basic services that are necessary for almost every business such as compute and storage services, cloud computing also helps in leveraging some of the most edge cutting technological innovations such as Machine Learning and artificial intelligence in the form of ‘Machine Learning as a service’.

In earlier days. Businesses found it to be very expensive not to mention talent demanding to implement Machine Learning. But now the trends are changing and with ‘Machine Learning as a service‘ cloud solution in the picture, businesses can get more out of the cloud.

One of the top cloud computing providers, Google Cloud platform is considered to be the leading MLaaS provider. This Google Cloud Machine Learning tutorial highlights all the top Google cloud machine learning solutions.

So, in this Google Cloud Machine learning tutorial, we will go over the following topics:

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What is Google Cloud Machine Learning as a Service?

Machine learning as a Service is a range of ready-to-use services that provide Machine Learning tools as a part of cloud computing services. Needless to say that these service include all the tools required for all basic machine learning steps such as data visualization, predictive analytics, data modeling APIs and more. The main focus of these services is that the users can directly get started with machine learning without needing to install any software or provisioning their servers. The users don’t handle the actual computation and implementation as it is handled by the MLaaS providers.

So, all in all, MLaaS is becoming a de-facto solution for fast and hassle free model training for businesses, be it large, small or mid-sized businesses.


Moving on with this Google Cloud Machine Learning Tutorial, lets now understand What Google Cloud Machine Learning is.

What is Google Machine Learning?

Google has been one of the pioneers of AI and ML research since anyone can remember, and the proof of that can be seen in Google’s outstanding products such as YouTube, Google Translator, Maps and of course Google search.

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Google prides itself with its MLaaS offerings that lets the users enjoy Google’s expertise in AI. Some of the Machine Learning services offered by Google are listed below:

  • Machine Learning Engine
  • Cloud Vision API
  • Cloud Translation API
  • Cloud AutoML
  • BigQuery ML

Let me give you a thorough walkthrough of all these services one by one, starting with Machine learning Engine:

If you have any doubts or queries related to Machine Learning, do post on Machine Learning Community.

Google Cloud Machine Learning Engine

Machine Learning EngineGoogle Cloud Machine Learning Engine is basically a training and prediction service that enables developers and data scientists to build superior and complex machine learning models and deploy in production. This service makes use of Tensor flow’s open source library. It also supports Scikit – learn and Keras framework which provides the users with the flexibility to choose their desired machine learning framework.

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Cloud AutoML

Cloud AutoML lets its users, with no or Cloud AutoMLlimited data science expertise or any ML knowledge, to train models on their data in an automated way. The organizations can just feed the data specific to their industry directly to the pre-built machine learning APIs and can expect results of great accuracy.

Even though Cloud AutoML is still in Beta phase, many use cases have already emerged for this MLaaS solution by Google. Cloud AutoML provides a simple GUI to train, analyze, improve and deploy models derived from custom data.

Cloud Vision API

Cloud Vision API - google cloud machine learning tutorialThis service is probably one of the most fascinating ML services by Google. What if I told you that you can scan a street sign in a stranger place in an unknown language and that mysterious text will not only be read by your phone but will also be translated for you? Wouldn’t that be cool? And thanks to Cloud Vision API, it’s not just a thought-provoking idea anymore, it’s actually a reality! Cloud vision API can classify pictures of cats and puppies but that’s not all it can do. It can recognize facial expressions and detect emotions such as surprise from those expressions and can also read the handwritten text. There are many more things that can be done using Cloud Vision API.

If you have any doubts or queries related to Data Science, do post on Machine Learning Community.

Cloud Translation API

Cloud Translation APIAs the name suggests, the cloud Translation API detects the language and translates it into another. The list of the supported languages for Translation API is long and it’s getting longer with time. This service comes in handy when the organization needs to integrate the services with third-party sites and applications using different languages.

If you have any doubts or queries related to AI, do post on AI Community.

BigQuery ML

Big Query is yet another service that is worth mentioning. It’s a data warehouse BigQuery MLservice that caters the data analysis requirements of the organizations or users. It lets its users create and execute ML models through standard SQL statements and commands. So one only needs to know SQL to be able to use BigQuery ML. It also speeds up the whole process as the data models are trained directly where the data stored, so there is no need to move data to other tools.

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In conclusion, we can see that Google doesn’t plan on leaving out any group of individuals when it comes to who can use Google’s MLaaS offerings. Google offers impressive ML solutions to Data engineers and Data Scientists with its Cloud ML Engine and TensorFlow. While Cloud AutoML extends the boundaries to any user. The cloud AI APIs, on the other hand, are cut and perfectly tailored for developers to provide them pre-packaged services. And Big Query ML offers a plethora of possibilities for analysts. With this we come to an end of this article, if you are looking to learn more about Google Cloud then do check out our Google certification training program.

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I hope you got to learn and take away something from this Google Cloud Machine learning tutorial. Keep visiting to learn more from our blogs while we make sure that we make your stay worthwhile!

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