The Google Cloud Platform
When compared to the AWS and Azure, the Google Cloud Platform comes in at a rather distant third place. The biggest component of the GCP is what is known as the “AI Hub.” This is a huge interface that consists of plug and play components, sophisticated AI algorithms, instant collaboration features, as well as the ability to import a large amount of datasets that have been stored with other Cloud Providers. Here are some of the key features of the AI Hub:
1) Component and Code Discovery:
Through this, you can access the following components:
■ Google AI;
■ Google Cloud AI;
■ Google Cloud Partners.
2) Collaboration:
This component helps to avoid duplication, especially if you are building a large scale AI project as part of a massive team effort. It possesses very granular types of controls, and even comes with a set of AI algorithms that you can use right out of the box.
3) Deployment:
This particular functionality allows for the full modification and customization of the AI algorithms that you are either planning to use or are in the process of using for your AI application. Once you have built your application, you can even host them on the platforms of other Cloud Providers as well.
The Google Cloud AI Building Blocks
The Google Cloud Platform (GCP) comes with many other tools as well, which are as follows:
1) The Google Cloud AutoML Custom Models:
The AutoML makes use of a very sophisticated Learning and Neural Network Architecture so that you can create a very specific AI application in a particular subdomain of Artificial Intelligence.
2) The Google Cloud Pre-Trained APIs:
With this, you can literally use specially trained APIs without first having your AI application learn to go through the entire training process. A great feature of these is that these specific APIs are constantly being upgraded to keep them optimized and refined for powerful levels of processing and speed.
3) The Vision AI and AutoML Vision:
With this kind of service, you can gain timely insights from the AutoML Vision or the Vision API models, which are actually all pretrained. It can actually be used to detect the emotion of an individual, especially if you are using your AI application for a sophisticated chatbot tool. Further, with the Google Vision API, you can even make use of both “RESTful” and “RPC API” calls. With these respective APIs, you can quickly and easily classify any sort of image that you may upload into your AI application. This is actually a service that has already been pretrained, and it consists of well over a million category types. It can be used to convert speech to text, and for incorporating Facial Recognition technolog)' into your AI system.
4) The AutoML Intelligence and Video Intelligence API:
This is a service with which you can track and classify objects in a video, using various kinds of AI models. You can use this service to track for objects in streaming video as well.
5) The AutoML Natural Language and Natural Language API:
Through an easy to use API, you can determine all sorts of “sentiment,” which include the following:
■ Entity Analysis;
Sentiment Analysis;
■ Content Classification;
■ Entity Sentiment Analysis;
■ Syntax Analysis.
You can even feed datasets into it in order to determine which ones are best suited for your AI application.
6) Dialogflow:
This is actually a software development service, in which a software development team can create an agent that can engage in a conversation with a real person, such as, once again, a chatbot. Once this has been done, you can launch your chatbot instantly across these platforms:
■ Google Assistant;
■ Facebook Messenger;
■ Slack;
■ The Alexa Voice Services.
7) Text to Speech:
With this, you can quickly and easily convert any human speech into over 30 different foreign languages and their corresponding dialects. In order to do this, it makes use of a Speech Synthesis tool called “WaveNet” in order to deliver an enterprise grade MP3 audio file.
8) Speech to Text:
This is simply the reverse of the above. With this, you can also quickly and easily convert the audio files into text by using the Neural Network algorithms that are already built into the Google Cloud Platform. Although these algorithms are quite complex in nature, they can be invoked quite easily and quickly via the usage of a specialized API. In this regard, over 120 separate languages are supported, as well as their dialects. Speech to Text can be used for the following purposes:
■ It can enable any kind of voice command in any sort of application;
■ It can transcribe call center conversations;
■ It can easily co-minge with other non-Google services that are AI related;
■ It can process audio in real time and convert speech to text from prerecorded conversations as well.
9) The AutoML Tables:
With this type of functionality, you can deploy your AI models on purely structured datasets. Although no specific coding is required, if it needs to be done, then you make use of “Colab Notebooks.” It works in a manner that is very similar to the Jupyter in the AWS.
10) Recommendations AI:
This is a unique service in that can deliver any type of product recommendations for a customer-related AI application, once again, like that of a chatbot.
We have now reviewed what the major Cloud Providers, the Amazon Web Services, Microsoft Azure, and Google offer in terms of Al-related services. We now examine some AI applications that you can build, making use of the Python source code.
