Top 8 Google AI Tools Explained
Google is not merely a search engine but much beyond that. It has quickly grown to bring innovations in several IT sectors. Whatever activity you want, Google is there to help you. Either in a shopping list or music tools, a smart voice helper, or advanced cultural applications.
In the past decade, Google has transformed completely to bring a new ecosystem for users.
There is no question Google is one of the biggest tech giants. It creates software tools for virtually any imaginable activity existent today. Who is responsible for IT development?
Probably, it is valuable to have your thoughts stored on your Keep or remember appointment through a calendar.
Does Google really have programs for programmers?
Why does it take time to see the next Terminator?
Google likes those who have an interest in advancing AI technologies.
Google AI Tools For Developers
1. TensorFlow(TF)
TensorFlow is the name of an open-source deep-learning library that Google offers to the world. It uses the machine learning part of artificial intelligence to improve its services.
With the help of Tensorflow’s ai tools and library, developers can build highly precise and well-defined Machine Learning (ML) systems.
At first, Google brought this open-source ML system for speech recognition. But at present, its main use is to help developers in creating artificial intelligence-based products.
Some of the best features of TensorFlow are:
Simple and free-to-use for high-level model creation.
Powerful enough to develop independent ML productions.
Research powers for the experimental uses·
At present, TensorFlow uses many programming languages, but the basic one is Python. In this way, all processes operate on the C++ modules in an augmented way.
TensorFlow library creates a graph of action following its name. Each node or Tensor stores data in a variety of shapes and sends it through branches or Flows.
After setting all functions beforehand, you can run the project. TF also provides its developers the ability to manage the final model through visualization instruments.
In simple words, if you need to develop a complicated system, TF is a better solution to do this job.
2. Machine Learning (ML) Kit
For developers creating a mobile app, an Machine Learning Kit can be very helpful. As you know, the creation of any big product needs consumer interest. This, in turn, requires surveys and event tracking.
Google provides you with a nice instrument for this purpose, Google Firebase which stores and processes a huge amount of data. Then adds analytics to synchronize the data with Big Query and Google Data Studio.
But, if the system requires a Machine Learning System, Machine Learning Kit is one of the best options. It is a set of pre-defined Application Programming Interfaces (APIs) for the most common mobile purposes.
Machine Learning Kit can be built-in for the app or taken from Google Cloud. Although the former can work without the internet at a fast pace, the latter is robust enough not to consume your mobile resources.
3. CoLaboratory
You come across a popular study tool with the name of Jupyter Notebook while working with Python. However, it’s quite useful and supports add-ons and instruments. But sometimes, you might face difficulty sharing or working on a file due to language dependencies and library limitations.
As there is always room for improvement, Google proposed CoLaboratory-Jupyter on Google Drive.
One of its main advantages is remote access to the developing files without depending on a local machine. Moreover, like any other Google document, you get an opportunity to work on the same file.
You can also code snippets for many generic tasks.
4. Google Open Source
In the 21st century, no one likes secured or secret codes. Hence Open Source is one of the most liked philosophies in today’s world. If you know about any up-gradation of technology and are willing to do forking experiments, go ahead.
You can share your results of up-gradation with the community and remain open to their bits of advice. They can take part in the developing process in this way and make their upgrades.
Google Open Source stimulates the creation of useful products without any commercial interest. Not only, with the help of Google Open Source, you can test your code but more than 2000 other projects.
You can use it to do meaningful research by investigating with the help of a few keywords.
Google AI Tools For Researchers
1. Google Datasets
The Machine Learning model needs a balanced dataset. The main and difficult part of the work is handling the raw data. Google has made it easy with its datasets. 64 high-level datasets can do ML tasks. All users have to complete their free survey before actually starting its use.
2. Google Datasets Search
Often you come across some atypical problems for which basic datasets are not competent enough. That’s why Google has brought something helpful, Google datasets search.
Dataset search helps in analyzing big datasets to answer any query.
It shows results in the relevance order; links are following the description and download. But, at present, this search engine is a beta version.
Google AI Tools For Organizations
1. Cloud Tensor Processing Unit (TPU)
Commercial products need increasing speed and decreasing local resources. Here comes Google Cloud to do the task.
All tools belonging to this category do the job, and Cloud TPU is no different. In simple words, it is a way to complete some large code computations with significant performance growth.
Google uses the TPU tool in some popular company products: Gmail and Calendar, etc. Depending on their power and pricing, there is a range of tensor Processing Unit versions.
Companies can scale their projects according to their need.
2. Cloud AI
This Google AI Tool is more about artificial intelligence. It works much like ML Kit, but in large systems, not confined to mere mobile applications.
It gives a chance to interact with more advanced technologies, not only basic ML solutions.
Few components of this cloud tool include:
AI Hub
It consists of well-maintained pipelines of artificial intelligence-based end-to-end projects. AI Hub uses some AI modules built by other teams and processes theme-related content published by Google AI, Google Cloud AI, and Google Cloud Partners.
AI Building Blocks
These have an array of typical use cases and needs. AI building blocks add language, conversation, vision, and structured data into custom applications.
One such example is recommendations AI, which helps to create advanced advisory systems based on consumer preferences.
AI Platform
Contrary to previous components, this project of Cloud AI contributes to the development of ‘thinking computer systems.’ Engineers on this platform can build their systems that have been used on the Google Cloud Platform.
Based on remote computing, the platform can train new models without significant code modifications. It supports many other Google tools for synchronizations, such as Google Cloud Platform, Big Query, Deep learning VM Image.
Cloud AutoML
Cloud AutoML is one of the most interesting yet upcoming projects in Cloud AI. Despite in beta version, Cloud AutoML's popularity is still growing. It consists of a set of Machine Learning tools that do specific operations like training data creation, AutoML tables, and AutoML Translation.·
To conclude, if you want to have some remote and productive artificial intelligence projects, Cloud AI will provide you with full support in this respect.
Final Thoughts
Google AI tools show consistent Google efforts to bring innovations in artificial intelligence tools. Whether you are a developer, researcher, or commercial worker, you can benefit from Google AI tools.
One of the main specialties of these tools is the integration among them. Hence you can store data in the Big Query, process it through a custom TF model, boost it via TPUs and share it through AI Hub.
The purpose is to facilitate and empower users with precise means of evaluating, collaborating, and improving their tailored machine learning models. It can result in enhanced productivity and better services.
