AI & Machine Learning


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.


91% of executives say AI will help them outpace their rivals, according to a Forbes insights survey

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.

Read more

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.

Where is it used?

Transportation

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.

Retail

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.

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.

Government

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.

Oil & 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.

Finance

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.

Powerful Google Cloud services to choose from

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.

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

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.

... and many more powerful ML services!

Google Award Winning Service Partner

We, at Cloud Ace have a lot of experience and expertise helping clients leverage Google's machine learning services to their fullest with custom tailored solutions. Also, no matter if you want to make your system or application smarter or if you want to run an application on the cloud, migrate your whole system or find a hybrid solution, we can help you do that. Our expertise is recognized by Google through various specializations and awards such as the 'Google Cloud Service Partner of the Year for Japan 2019' award.

With our expertise and experience we can provide you with professional expertise and help you leverage the power of Google Cloud's resources to the fullest.

Case Study

Persol Career

Is operating various popular job listing sites in Japan such as doda.jp.

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.

[...] 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.

Read the full case study here