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Machine learning APIs for Google Cloud Platform

Essential Characteristics of Cloud Computing as Digital Transformation

By pwsadmin | September 25, 2020

A survey of 2,000 executives conducted by Cognizant in 2016 identified the top five ways digital transformations generate value:      Accelerating speed to market      Strengthening competitive positioning      Boosting revenue growth      Raising employee productivity      Expanding the ability to acquire, engage, and retain customers   Digital transformation is also a cultural change. Cloud Computing as Digital Transformation Since cloud…

Embrace Transformation

By pwsadmin | September 22, 2020

From a business perspective, differentiating business processes and quality customer service are central to overall success. Business leaders must therefore clearly identify and measure how information technology contributes to the value of every key business process. They must also know how to most cost effectively use IT when the task is merely the management of…

Computer Vision Advances Zero-Defect Manufacturing

By pwsadmin | July 25, 2020

by Kevin L. Jackson Electronics manufacturers operate in a challenging environment. It’s hard enough to keep up with the ever-accelerating rate of change in the industry. Now customers want increasingly specialized product variations in less time and of higher quality. Meeting this demand for increased product variation can seriously impact the bottom line. Such variability increases…

Real-Time Analytics Power the Roadway of the Future

By pwsadmin | July 25, 2020

By Kevin L. Jackson The complexities of citywide traffic are pushing the limits of existing transportation management systems. Outdated infrastructure is based on proprietary, single-purpose subsystems, making it costly to acquire, operate, and maintain. And current roadways are simply not prepared for the future of autonomous vehicles. Enter the SPaT Challenge, an initiative encouraging cities and…

Thriving on the Edge: Developing CSP Edge Computing Strategy

By pwsadmin | March 6, 2020

Communications Service Providers (CSPs) are facing significant business model challenges. Referred to generally as edge computing, the possibilities introduced by the blending of 5G networks and distributed cloud computing technologies are redefining how CSPs operate, partner, and drive revenue. A new Ericsson Digital whitepaper entitled, “Edge computing and deployment strategies for communication service providers,” addresses these challenges…

SourceConnecte! Marketplace With A Mission

By pwsadmin | March 6, 2020

Earlier this year, GC GlobalNet launched a new breed of B2B e-commerce sites. Curated by Kevin L. Jackson, SourceConnecte (with an “e”) went live with three strategic goals in mind: Efficiently leverage modern social media technologies to facilitate value-based interactions between enterprise buyers and vetted suppliers; Establish a protected interactive environment capable of supporting high-value B2B e-commerce negotiations…

Potential vs. Reality: Is Edge Computing Real?

By pwsadmin | January 19, 2020

Edge computing provides compute, storage, and networking resources close to devices generating traffic. Its benefits are based on an ability to provide new services capable of meeting stringent operational requirements by minimizing both data latency and the need for bandwidth. Based on Google trend data, searches for the term has also grown substantially over the…

Enabling Digital Transformation

By pwsadmin | December 22, 2019

Digital transformation integrates technology into all areas of an organization’s business or mission. Its fundamental purpose is to create and deliver innovative and industry-changing products and services to a global customer base. This outcome requires the seamless two-way flow of data and information between internal business processes and external processes that interact with customers, business…

The ThinkShield Story Part 1: The Challenge

By G C Network | October 24, 2019

The cybersecurity challenge seems to be growing daily. Threats are becoming more sophisticated, and attacks are becoming more destructive while the corporate world’s response seems to resemble a deer in headlights. Recent examples of this dangerous state of affairs include[1]: A data breach of a US Customs and Border Protection surveillance contractor that led to…

CIO dream team: Who’s in and why?

By G C Network | October 12, 2019

Today’s CIO navigates the twin challenges of enabling new business models and managing rapid technological change. Cloud computing strategies are now table stakes. CIOs must make complex decisions about using public and private clouds, on-premises enterprise systems, Internet of Things, edge computing, and many other user experience outlets. Cloud-enabled digital transformation can’t happen without the…

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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