GCP Machine Learning Guide

apps admin blog (6) The Google Cloud Platform or "GCP" is a set of assets supported by Google data centers around the world. Because these assets exist on the cloud, there's no need for enterprises to install new hardware to make the most of them, instead, they can simply sign up for a license, and adjust their computing infrastructure as the business grows.

There are plenty of things that set the GCP apart from other cloud-based computing services online today. For instance, the GCP comes with the incredible security of the Google Network, which uses cutting-edge encryption technology to keep your data safe. However, perhaps one of the most compelling features of the Google Cloud Platform is its comprehensive suite of innovative machine learning services.

While other cloud computing systems might focus entirely on big data, computing, and communication, Google provides users with access to all the latest developments the technology giant makes in the world of artificial intelligence, ranging all the way from video transcription, to natural language processing.

Machine Learning Services on the GCP

Machine learning is an advanced functionality that helps software to perform tasks without the need for constant human oversight. In other words, your computer systems learn how to deliver the results you're looking for, by examining the data in a neural network.

Considered to be a sub-category of the larger "Artificial Intelligence" framework, machine learning involves using a range of advanced statistical techniques like deep learning and analysis to process huge amounts of information. As a leader in the AI marketplace, Google gives GCP users a chance to tap into the algorithmic models that power their own machine learning strategies, to unlock actionable insights into their business.

Because machine learning models require huge amounts of data to function properly, the Google Cloud Platform offers the perfect environment in which for these services to thrive. With access to astronomical amounts of information, Google's machine learning models can adapt to marketplace trends, and unlock new ideas quickly, and effectively.

The potential for machine learning, when used correctly, is practically limitless, for instance, in the modern world, ML algorithms can be found in:

Hospitals that use computers to categorize images and diagnose patients
Programs that evaluate telephone conversations to flag possibly instances of fraud
Websites that recommend products based on user behavior
Apps that predict weather patterns based on climate conditions
Tools that translate language in audio or text files

Machine Learning and AI on the Google Cloud Platform

For years now, machine learning has formed a cornerstone of the Google internal framework, thanks to their need for accurate and automated data-driven systems. By unlocking access to things like the Tensorflow ML framework, Google ensures that innovators in the cloud space can make their companies more intuitive, creative, and productive with the latest machine learning strategies.

The GCP machine learning portfolio comes packed full of pre-trained models and make-your-own API services so that companies can choose the perfect AI strategy. What's more, Google's neural-net service has better accuracy and training performance than any other large-scale deep learning system on the market to use. So, what exactly can GCP users do with machine learning through Google?

Google Cloud AutoML

Cloud AutoML "Alpha" is the GCP Suite of machine learning products designed to offer developers a new opportunity to access the many benefits of AI-approved systems for business development. You don't need a lot of training or expertise in the machine learning environment to leverage Google's "transfer learning" technology, instead, you can simply tap into the solutions that Google have already built for you.

AutoML Vision is the first product to hit the market, which allows companies to easily train and create their own visual models for state of the art data management. Even the GUI for the AutoML setup is simple, so anyone can make the most of the latest AI products.

Features include:

Human labeling integration: Images need labels to be organized correctly. Fortunately, if you have visuals but no labels yet, Google provides a team of in-house labelers who can classify your images for you, making it easier to train and implement your new AI system in no time.
State of the art technology: AutoML uses the latest transfer learning technology to produce the highest-quality models for customers. The system has already achieved exceptional performance on databases like ImageNet and CIFAR.
Full Integration: Like many of the solutions in the Google Cloud Platform portfolio, AutoML integrates with a range of other services all the way from Google Cloud Storage, to collaboration tools. This ensures that you can gather as much data for your machine learning model as possible from different parts of your business.

Google Cloud Machine Learning Engine

For machine learning services at a large enterprise level, Google has the "Cloud Machine Learning Engine". This system is designed to help users build high-quality AI models, and deploy them within as little time as possible. This managed service delivered by the Google team simplifies the process of building large-scale machine learning models for a range of different scenarios.

Available to test for free, the Engine allows modern companies to train new models with huge amounts of data accessed from other Google Cloud services, such as Google Cloud Storage, Google Cloud Datalab, and Google Cloud Dataflow. Models can be designed to predict important outcomes in the business world. For instance, the Machine Learning Engine currently helps Airbus Defense and Space to correct visual anomalies in the images they capture from satellites with speed and accuracy. Features include:

Server-Side Preprocessing: Users can send raw data through Google Cloud to machine learning models still in production - reducing their local computation power. This also helps to reduce the risk of data skew occurring through different strategies for preprocessing in prediction and training.
Automatic Resource Provisioning: As a managed service model, the Machine Learning Engine allows GCP users to focus on developing their machine learning models without having to worry about infrastructure, monitoring, and provisioning. The training infrastructure available supports TPUs, GPUs, and CPUs automatically.
Integration: Since machine learning thrives on information, many of the systems on the GCP are designed to work seamlessly together. The Machine Learning Engine works alongside Cloud Storage to maintain data securely, and Cloud Dataflow for feature processing services.
Hypertune: Google helps companies to achieve incredible business results faster by automatically tuning parameters for deep learning with HyperTune.
Support for various frameworks: The online prediction system supports various frameworks to serve clustering, regression, and classification models.
Portability: With the TensorFlow SDK customers of the Google Machine Learning Engine can train models locally on sample data sets, or move to the Cloud Platform for additional scale.

Google Cloud Speech Recognition

Data comes in many different forms - not just convenient text documents. The GCP's machine-learning feature "Speech to Text" applies advanced neural network models into business applications, through an easy-to-understand API. This API can understand more than 1120 languages and variants so that enterprises can connect with a diverse user base.

Not only does the Speech-to-Text system work on pre-recorded audio stored on the Google Cloud system, but it can also process real-time streaming content in a business environment too. Some of the most compelling features include:

A Global vocabulary: Support for 120 languages
Automatic Speech Recognition: The Google Deep Learning neural network uses ASR to power services like speech transcription and voice search.
Word hints: Speech recognition can be designed to work within specific contexts, by adding sets of phrases to the system that are regularly spoken. This means that each company can create a speech recognition strategy that suits their enterprise.
Content filtering: Use a robust machine learning strategy to filter inappropriate content in text results for certain languages. Google even automatically adds punctuation to transcriptions.
Range of models to choose from: Google gives GCP users the chance to choose from four pre-built speech-to-text models, each equipped with noise robustness to overcome issues of background sound.

Google Cloud Dialogue Flow (Enterprise Edition)

The Speech-to-Text system offered by Google isn't the only way that the GCP makes the most of audio information. The recently-developed Dialogflow application uses the power of machine learning to build natural conversational experiences into various platforms and devices. Essentially, Dialogflow is a comprehensive development suite providing companies with the tools they need to build conversational strategies like chatbots for mobile applications, messaging platforms, websites, and IoT devices.

With Dialogflow, businesses can create digital service agents that can sustain rich and organic interactions with users. The system is powered by machine learning, so it can recognize the context and intent in user speech. This means that the modern enterprise can finally overcome the robotic nature of automated response systems. Features include:

Integrated code editor: The Dialogflow app comes with an integrated code editor that allows users on the GCP to build their own serverless applications linked to conversational interfaces through the Cloud Functions for Firebase.
Multi-language cross-platform support: Dialogflow is designed to work with the omnichannel interfaces of the modern enterprise. This means that it supports more than 20 different languages and works seamlessly on 14 popular platforms too. With Dialogflow, you can reach a wider range of audience members.
Cloud Speech Support: The Dialogflow system is powered by the innovative Google Cloud Speech service, which means that you can expand your conversational strategy to recognize voice interactions, as well as text messages.

Google Cloud Image Analysis

Just as data can expand beyond the textual world into the audio world - there's also information to be gained from diving into the details of images and videos. The Google Cloud Vision API is one of the open-source solutions that the GPU has to offer modern business users who want to understand the content of large packages of images easily.

Using an effective REST API, the Google Cloud Vision system can classify images into thousands of different categories and detect individual faces and objects within images too. The machine learning algorithm can even detect and read printed words within pictures. Features include:

Label detection: Detect a range of different categories within an image. The Cloud Vision API can easily tell the difference between cars, animals, and people. It can even pick brand logos out of a photograph.
Explicit content management: Most companies don't have the time to go through thousands of images to make sure that explicit content doesn't end up in the system. The Cloud Vision service does this for you, detecting adult or violent content in an instant.
Face detection: While GCP doesn't have a system for facial recognition yet, the Cloud Vision service can detect faces within an image, and pinpoint facial attributes like emotional state, or whether someone is wearing glasses. It can also search the vast Google databases on the web for similar pictures.
Character recognition: Optical character recognition means that businesses can extract text from an image, with support for dozens of languages. All that, and it integrates with other GCP tools like Google Cloud Storage too.

Google Cloud Video Analysis

Taking machine learning and image recognition to the next level is the Google Cloud Video Intelligence service. A REST API that can be added to business software or applications, Cloud Video Intelligence makes it easier for brands to search through video content to find important statements and images. Essentially, it makes videos searchable by identifying keywords, extracting metadata, and annotating the content within the video.

With Cloud Video Intelligence, users can search every moment of their video files in a matter of minutes, without the need for extensive manual work. You can even detect specific entities within the video by searching for nouns like "car", "flower" or "dog". Features of the video intelligence system include:

Shot change detection: The machine learning algorithm created by Google can determine when the focus of the video changes or the scene switches to something new.
Explicit Content Detection: Because the Video API can quickly pinpoint adult content or violence in a video, it can make it easier to edit content before publication.
Video Transcription: The Google GPU machine learning system can automatically transcribe video content into English text. For now, there's only the English language to choose from, but Google does plan on adding more options at a later stage.

Google Cloud Translation API

Today's companies are becoming increasingly dispersed, with remote and global employees coming together to work on projects from around the globe. For help making sure that everyone in an organization can speak the same language, the Cloud Translation API can be easily implemented into a business network, to automatically translate arbitrary strings into supported language formats.

Google's Translation API is incredibly responsive, which means that it works well with applications and websites alike - ensuring that today's companies can integrate Translation strategies into their infrastructure quickly and effectively. Features include:

Developer Access: Because it comes in an easy-to-use REST API format, the Google Cloud Translation service can be added to existing programs and applications by modern network developers using languages like Python, and Ruby on Rails.
Global Support: The Translation API supports over 100 languages, alongside thousands of different language pairs. What's more, companies can send information in HTML and receive HTML with the translated text included in the code. This means that text doesn't need to be extracted to be translated.
Language Detection: The REST API automatically detects which language you're using, so you can quickly translate it into your preferred script.
Constant Cloud Updates: Because the Translation API is powered by machine learning, it's constantly learning from human translation examples and log analysis. That means that your API is always improving.

Google Cloud Natural Language Processing

Natural Language Processing is one of the most important technologies in the machine learning world, as it's the key to bridging the gap in communication between computers and human beings. Google's Natural Language API can upgrade basic machine learning models, by helping them to better-understand natural and organic speech.

The Natural Language API can be used to extract information about places, people, events, and more that have been mentioned in articles or text documents. In fact, it's even an effective way to learn more about your brand's reputation by evaluating customer sentiment on social media. Features include:

Syntax analysis and Sentiment analysis: Extract sentences and tokens and identify specific areas of speech within a textual document. Google's machine learning service can even help businesses to understand the mood behind a block of text with sentiment analysis.
Entity recognition and content classification: Automatically identify labels by types such as "location", "person" "product" or "organization", then classify documents into one of over 700 categories.
Multiple language choices: With the GPU's machine learning toolkit, users can analyze text in various languages, including Chinese, Japanese, Spanish, English, German, Korean, Italian, and more.

The Benefits of Machine Learning for Businesses

If businesses want to remain competitive in an agile and ever-changing marketplace, then they need to be open to the concept of new and disruptive technology like AI. A cloud environment like the GPU is the perfect solution for agile companies who want to take advantage of the latest trends, as it allows organizations to explore updates and developments without the expense of installing new hardware.

For data scientists looking for an opportunity to build future-proof models for success, machine learning models on the Google Cloud Platform could be the ultimate step towards success. What's more, the services available in association with machine learning are constantly growing more complex. For instance, in addition to the solutions mentioned above, Google also offers:

Google Cloud TPUs: When it comes to making the most of machine learning, sometimes your hardware can make a serious difference. Google's TPUs are a family of hardware accelerating solutions designed to help add scale and speed to your machine learning workloads.
Job Discovery: Having the right skills in your enterprise will always be key to success. The Google Cloud Job Discovery system uses machine learning to provide an intuitive job search experience that anticipates what job seekers are looking for and helps them to find results that match their skillsets.
Speech Synthesis: The Google Cloud "Text to Speech" service allows developers in the machine learning environment to synthesize and create natural speech for their IVRs and other systems. The system comes equipped with 32 voices to choose from, with multiple variants and language options.

According to IDC, 40% of all digital transformation initiatives will be linked to AI and machine learning capabilities. These new upgrades will give brands the chance to stay on the cutting edge by gaining intelligence from large amounts of data. As the Google Cloud Platform's focus on machine learning continues to grow, it's safe to say that machine learning is no longer a concept that belongs only to the most tech-savvy companies in the marketplace.

Are you interested in the possibilities of machine learning? Reach out to Coolhead Tech today to discover the GCP strategy that's right for you.