Machine learning APIs for Google Cloud Platform

Google Cloud Platform (GCP) is considered to be one of the Big 3 cloud platforms among Microsoft Azure and AWS. GCP is widely used cloud solutions supporting AI capabilities to design and develop smart models to turn your data into insights at a cheap, affordable cost.

The following excerpt is taken from the book ‘Cloud Analytics with Google Cloud Platform‘ authored by Sanket Thodge.

GCP offers many machine learning APIs, among which we take a look at the 3 most popular APIs:

Cloud Speech API

A powerful API from GCP! This enables the user to convert speech to text by using a neural network model. This API is used to recognize over 100 languages throughout the world. It can also support filter of unwanted noise/ content from a text, under various types of environments. It supports context-awareness recognition, works on any device, any platform, anywhere, including IoT. It has features like Automatic Speech Recognition (ASR), Global Vocabulary, Streaming Recognition, Word Hints, Real-Time Audio support, Noise Robustness, Inappropriate Content Filtering and supports for integration with other APIs of GCP.

 The architecture of the Cloud Speech API is as follows:

In other words, this model enables speech to text conversion by ML.

The components used by the Speech API are:

·         REST API or Google Remote Procedure Call (gRPC) API

·         Google Cloud Client Library

·         JSON API

·         Python

·         Cloud DataLab

·         Cloud Data Storage

·         Cloud Endpoints

The applications of the model include:

·         Voice user interfaces

·         Domotic appliance control

·         Preparation of structured documents

·         Aircraft / direct voice outputs

·         Speech to text processing

·         Telecommunication

It is free of charge for 15 seconds per usage, up to 60 minutes per month. More than that will be charged at $0.006 per usage.

Now, as we have learned about the concepts and the applications of the model, let’s learn some use cases where we can implement the model:

·         Solving crimes with voice recognition: AGNITIO, A voice biometrics specialist partnered with Morpho (Safran) to bring Voice ID technology into its multimodal suite of criminal identification products.

·         Buying products and services with the sound of your voice: Another most popular and mainstream application of biometrics, in general, is mobile payments. Voice recognition has also made its way into this highly competitive arena.

·         A hands-free AI assistant that knows who you are: Any mobile phone nowadays has voice recognition software in the form of AI machine learning algorithms.

Cloud Translation API

Natural language processing (NLP) is a part of artificial intelligence that focuses on Machine Translation (MT). MT has become the main focus of NLP group for many years. MT deals with translating text from the source language to text in the target language. Cloud Translation API provides a graphical user interface to translate an inputted string of a language to targeted language, it’s highly responsive, scalable and dynamic in nature.

This API enables translation among 100+ languages. It also supports language detection automatically with accuracy. It provides a feature to read a web page contents and translate to another language, and need not be text extracted from a document. The Translation API supports various features such as programmatic access, text translation, language detection, continuous updates and adjustable quota, and affordable pricing.

The following image shows the architecture of the translation model: 

In other words, the cloud translation API is an adaptive Machine Translation Algorithm.

The components used by this model are:

·         REST API

·         Cloud DataLab

·         Cloud data storage

·         Python, Ruby

·         Clients Library

·         Cloud Endpoints

The most important application of the model is the conversion of a regional language to a foreign language.

The cost of text translation and language detection is $20 per 1 million characters.

Use cases

Now, as we have learned about the concepts and applications of the API, let’s learn two use cases where it has been successfully implemented:

·         Rule-based Machine Translation

·         Local Tissue Response to Injury and Trauma

We will discuss each of these use cases in the following sections.

Rule-based Machine Translation

The steps to implement rule-based Machine Translation successfully are as follows:

1.   Input text

2.   Parsing

3.   Tokenization

4.   Compare the rules to extract the meaning of prepositional phrase

5.   Find word of inputted language to word of the targeted language

6.   Frame the sentence of the targeted language

Local tissue response to injury and trauma

We can learn about the Machine Translation process from the responses of a local tissue to injuries and trauma. The human body follows a process similar to Machine Translation when dealing with injuries. We can roughly describe the process as follows:

1.   Hemorrhaging from lesioned vessels and blood clotting

2.   Blood-borne physiological components, leaking from the usually closed sanguineous compartment, are recognized as foreign material by the surrounding tissue since they are not tissue-specific

3.   Inflammatory response mediated by macrophages (and more rarely by foreign-body giant cells)

4.   Resorption of blood clot

5.   Ingrowth of blood vessels and fibroblasts, and the formation of granulation tissue

6.   Deposition of an unspecific but biocompatible type of repair (scar) tissue by fibroblasts

Cloud Vision API

Cloud Vision API is powerful image analytic tool. It enables the users to understand the content of an image. It helps in finding various attributes or categories of an image, such as labels, web, text, document, properties, safe search, and code of that image in JSON. In labels field, there are many sub-categories like text, line, font, area, graphics, screenshots, and points. How much area of graphics involved, text percentage, what percentage of empty area and area covered by text, is there any image partially or fully mapped in web are included web contents.

The document consists of blocks of the image with detailed description, properties show that the colors used in image is visualized. If any unwanted or inappropriate content is removed from the image through safe search. The main features of this API are label detection, explicit content detection, logo and landmark detection, face detection, web detection, and to extract the text the API used Optical Character Reader (OCR) and is supported for many languages. It does not support face recognition system.

The architecture for the Cloud Vision API is as follows:

We can summarize the functionalities of the API as extracting quantitative information from images, taking the input as an image and the output as numerics and text.

The components used in the API are:

·         Client Library

·         REST API

·         RPC API

·         OCR Language Support

·         Cloud Storage

·         Cloud Endpoints

Applications of the API include:

·         Industrial Robotics

·         Cartography

·         Geology

·         Forensics and Military

·         Medical and Healthcare

Cost: Free of charge for the first 1,000 units per month; after that, pay as you go.

Use cases

This technique can be successfully implemented in:

·         Image detection using an Android or iOS mobile device

·         Retinal Image Analysis (Ophthalmology)

We will discuss each of these use cases in the following topics.

Image detection using Android or iOS mobile device

Cloud Vision API can be successfully implemented to detect images using your smartphone. The steps to do this are simple:

1.   Input the image

2.   Run the Cloud Vision API

3.   Executes methods for detection of Face, Label, Text, Web and Document properties

4.   Generate the response in the form of phrase or string

5.   Populate the image details as a text view

Retinal Image Analysis – ophthalmology

Similarly, the API can also be used to analyze retinal images. The steps to implement this are as follows:

1.   Input the images of an eye

2.   Estimate the retinal biomarkers

3.   Do the process to remove the effected portion without losing necessary information

4.   Identify the location of specific structures

5.   Identify the boundaries of the object

6.   Find similar regions in two or more images

7.   Quantify the image with retinal portion damage

You can learn a lot more about the machine learning capabilities of GCP on their official documentation page.

If you found the above excerpt useful, make sure you check out our book ‘Cloud Analytics with Google Cloud Platform‘ for more information on why GCP is a top cloud solution for machine learning and AI.

( This sponsored post is part of a series designed to highlight recently published Packt books about leading technologies and software applications. The opinions expressed are solely those of the author and do not represent the views of GovCloud Network, GovCloud Network Partners.)

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