AI and Machine Learning

Learn about the numerous powerful AI and Machine Learning services on Google Cloud Platform


Why does it matter?

Machine Learning, as a subset of the wider field of Artificial Intelligence, aims to create computer applications that can outperform humans in certain tasks by making them smart and being able to learn and improve by themselves.

It is already being used in a vast variety of businesses across many industries and in the public sector as well. There are many use cases ranging from predicting user behaviour in e-commerce, optimizing delivery routes up to hospitals analyzing patient data, saving time and cost in the process.


6 out of 10 C-level executives surveyed by Forbes Insights believe AI is a key enabler of their organization's future success. 

4 out of 5 of those organizations have AI programs in place or are currently piloting them.

74% have 10 or more separate initiatives underway. 


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Where is it used?



Banks and other businesses in the financial industry use machine learning to identify important insights in data and prevent fraud and more. This enables identify valuable investment opportunities and helps investors know when and what to trade. On the other side data mining can reduce risks by identifying clients with high-risk profiles and can detect fraud.



Government agencies such as public safety and utilities have a particular need for machine learning since they work with all kinds of data that can be analyzed and used for predictions or forecasting. Machine learning can also help detect fraud and minimize identity theft.


Health care

Machine learning is vastly used in many health research fields and is especially growing rapidly trend in the health care industry, thanks to advancements in wearable devices and sensors that can use data to assess a patient's health in real time. The technology can also help medical experts analyze such data to identify health risks and prevent them.



By analyzing your purchase and browsing history through machine learning, websites can recommend items you might like and forecast purchase behaviour at a larger scale. Retailers rely on machine learning to capture data, analyze it and use it to personalize a shopping experience, implement a marketing campaign, optimize prices and supply demanding and more.



Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of machine learning are important tools to delivery companies, public transportation and other transportation organizations.


Oil and Gas

Machine Learning is also being used in the oil in gas industry for finding new energy sources, analyzing minerals in the ground, streamlining oil distribution to make it more efficient and cost-effective and for many other purposes. This field is quickly expanding in research and adaption of machine learning and AI.

Why use Google Cloud?


Performance and accuracy

It's no secret that Google owns the largest data sets. This plays a huge role for its machine learning models as they can train them with a lot more data than any other company, thus pushing the foundries for accuracy and performance, for example in analyzing images, written text, language and a lot more.

This high level of accuracy is what increasingly leads global enterprises such as Airbus to use Google's machine learning services. They were able to reduce their error rate from 11% to 3% in the critical process of correcting satellite image maps. This makes a big difference, especially for companies at this scale and there are a lot more examples which you can find below on this page.


Easy to use for everyone

Machine Learning can sound intimidating for many businesses who have little knowledge of it and aren't using it yet. However, with Google you don't have to be a machine learning engineer to get started. Google provides its machine learning services as pre-trained and ready-to-use modules that anyone can understand and set up for common use cases.

On the other side of the spectrum Google offers as much freedom and customization as you desire with Cloud AutoML that lets you create your own machine learning models even with little technical knowledge. And if you are a developer and want want to create a machine learning model from scratch, TensorFlow has you covered with a comprehensive platform, frameworks and the biggest and most active machine learning community worldwide.

What can it do?

Google Cloud Platform incorporates a vast variety of machine learning APIs in combination with platforms and frameworks that are all quite powerful, customizable and all work seamlessly together. The spectrum is broad and includes models for image, video, text and speech analysis.

In addition to these machine learning APIs, Google also includes additional machine learning options for its other services such as the datewarehouse BigQuery which is used for analyzing data and features intelligent optimizations through machine learning.


Image by Google


Vision API

Google's image processing service "Vision API" and offers powerful pre-trained machine learning models that can detect objects, faces, handwritten text, brands and label them. It's a very powerful and useful machine learning API for numerous reasons.

With Vision API you can for example implement features in your applications that let users upload their own images and provide purchase suggestions to enhance user experience. By detecting handwritten text (over 50 languages supported currently) you can also quickly analyze millions of documents and automate workflows. Another example is the detection of explicit content, which can make your applications and services more secure and safe for all users.

Vision API is one part of Google's Vision AI, which also includes AutoML Vision. This machine learning API gives you the option to create and train your own models. 

If you want to try out the API or read more click here.


Image by Google


Speech-to-Text API & Text-to-Speech API

Google's machine learning services offer two APIs that can convert speech to text and vice versa, using highly pre-trained and optimized machine learning models powered by neural network technology. The Speech-to-Text API currently supports 120 languages and the Text-to-Speech API supports over 30 languages and 180 voices.

Both APIs can work with in real-time enabling for example transcription of conferences and can be used in call centers for automization. There are a lot of use cases in which these APIs can be used effectively.

Try and and learn more about the Speech-to-Text and Text-to-Speech API.



Image by Google


Natural Language API

Google's Natural Language API is a machine learning powered service that lets you derive insight from unstructured text. Using the Natural Language API you can extract information about people, places, events and a lot more.

Popular use cases are automated customer and user review analytics to get a better understanding of how consumers and users respond to your products and services on social media or on review sites for instance. It can be also used to automize workflows as it can identify common entries in receipts and invoices such as dates, phone numbers, companies, prices and more.

In contrast to this pre-trained API, Google also offers the AutoML Natural Language service which lets you build and train your own model if you need a high level of customization.

Click here to learn more.


Image by Google


Translation API

The Google Translation API is perhaps the machine learning API that most people are familiar with through Google Translate as it uses essentially the same technology. However with this API lets you translate texts automatically at Google scale. The Translation API can not only translate texts, but also identify the source language, supports glossaries for customization and better translation accuracy and is quite affordable.

If that is not enough, Google also offers  AutoML Translation which lets you build your own model with the language pairs that you need and your own data sets to achieve a model that is highly trained and tailored to your use case.

Click here to learn more.


Image by Google


Video Intelligence API

The Video Intelligence API is the same technology that is being used in YouTube and offers pre-trained machine learning models that automatically recognize a vast number of objects, places and actions in stored and streamed videos. It detect over 20,000 objects, places, actions and can also distinguish scene changes and extract all sorts of metadata.

The Video Intelligence API is great for video content services as it can make your application smart and improve user experience. On the other side it can be used to automate workflows that include video content.

And as it is with the other machine learning APIs, this one features an AutoML version as well, which lets you build your own customized model.

Click here to test it out and learn more.


Image by Google


Cloud Talent Solution

Google also provides AI solutions that help companies find the right candidates faster and easier. Highly trained machine learning models can do job search queries, interpret job descriptions and match them with your requirements to speed up and increase the quality of hiring processes. 

Click here to learn more.


Image by Google



TensorFlow is a free and open-source software library and platform for machine learning. It was developed by Google in 2015 to provide strong support for machine learning and deep learning and advanced numerical computations across different scientific domains and is integrated in Google Cloud Platform.

The platform features a comprehensive set of tools such as many high-level APIs and frameworks that can be used to develop machine learning models and neural networks for production and for research.  Because of that it is easy to test and deploy models directly in the cloud and use them in production. 

By offering cutting-edge technology on a platform that is being frequently updated to address modern issues, yet for free and open-source, TensorFlow grew a large community of researchers and companies who are contributing and developing projects, such as Google itself, Coca-Cola, airbnb, Twitter, intel, NVIDIA, Uber, Snapchat just to name a few. Data such as the one from The Data Incubator suggest that TensorFlow is by far the most popular platform in its field.

Click here to learn more.


Image by Google


BigQuery ML

BigQuery is Google's highly powerful data warehouse and offers high scalability, allowing queries on billions rows of data and sizes up to terabyte and petabytes. It is popular among data scientists that work with large amounts of data sets. However they might not be experts in machine learning and can't train machine learning models with all their data sets. On the other hand, machine learning engineers might not be able to take advantage of huge data sets with limited knowledge in this field.

BigQuery ML enables anyone to train and build machine learning models using BigQuery data sets with easy queries so that data sets can be used to their full potential, even with limited knowledge.

It is a great and easy way to for example gain intelligence by creating forecasting machine learning models with existing data sets.

Click here to learn more.

BigQuery ML

Image by Google

How we can help you

We at Cloud Ace have a lot of experience of developing systems and applications on Google Cloud Platform and can help you with any kind development request. In fact our expertise was recognised by Google in 2018 and we won the Google Cloud Application Development Partner of the Year.

We also have a Google Cloud Specialization in Machine Learning and Application Development, proving our expertise and experience in these specific fields.


Persol Career Co., Ltd.

Is operating various popular job listing sites in Japan such as

The company used the Google Natural Language API to automatically analyze job description texts, scan them for relevant keywords and turn them into searchable tags to improve search results and user experience.

  • Consulting
  • Web Services

[...] when we tested the Google Natural Language API on Google Cloud Platform we were impressed by the results and decided that is suited for the purpose of this project.

Especially the precise analysis of sentence structures is impressive and makes us excited to see how it will perform and further improve in the future.

If you want to read more case studies, please check out our case studies page.

Case Studies

Whether you want to get started with AI and machine learning or already have an ongoing project and need technical support, we can help you out.

Check out our "GCP Comprehensive Support" package or contact us directly using the contact form below. We will get back to you as shorty.


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