There’s no question that Google is an absolute giant in the IT world. It creates various software resources for almost every imaginable field of operation that exists today. Whatever you might seek, Google probably has a solution.
If we’re talking about Artificial Intelligence, Google cares about those who are interested in that field as well. Today we’re going to talk about the most important tools in AI. This article is going to be divided by categories of people who might be interested in some specific tools.
With more developers plunging into the AI world seeing its usefulness, Google AI provides different tools to create artificial systems, neural networks, and multilayered projects.
1. TensorFlow (TF)
Currently Google’s TensorFlow is the world’s most famous deep learning library. Google’s software uses machine learning to boost the search engine, translation, image captioning and suggestions in all of its items.
TensorFlow is the best library of all, since it is designed to be accessible to all. Tensorflow library integrates numerous APIs to be applied to deep learning architecture such as CNN or RNN. Itis based on graph computing which enables the developer to visualize with Tensorboad the creation of the neural network. This tool is useful for debugging the programme. Tensorflow is finally built for scale deployment. It will run on both CPU and GPU.
Relative to the other deep learning platform, Tensorflow attracts the greatest popularity on GitHub.
Checkout TensorFlow to learn more!
2. ML Kit
This tool can be of great help for developers of mobile apps. You should bear in mind consumer interests when you want to make a really big product. Most obvious ways to do so are to conduct event tracking, surveys, etc. For these features Google recommends a very nice tool: Google Firebase. This allows a large amount of user data to be stored and analyzed, incorporates some lightweight analytics and synchronizes with Big Query (data warehouse) and Google Data Studio (business intelligence).
ML Kit will help you achieve success in the tasks that are guided by the machine learning techniques behind it such as,
- Language Identification
- Text Recognition
- Image Scanning and Labeling
- Face Recognition
- Smart Replies
- Bar-code Scanning
- Custom Model Integration with TensorFlow Lite
3. Google Open Source
There is a growing need to move it to the next level because newer and better tech evolves every day. When developers start producing open source code only then the community can engage actively and help improve and expand on it. Through accessing its repository, developers may change and scale the code with freely available code, often resolving complex process problems.
Google has promised to put together developers by enabling them to freely make their code available to anyone interested in the concept behind it.
Checkout Google Open Source to learn more!
4. Fairness Indicators
It is a tool that provides metrics in a machine learning system to measure fairness. Powered by TensorFlow, the aim is to remove any bias from a machine learning system while improving its fairness and reducing unequal biases from affecting systems and organisations. Google built this with all sorts of businesses in mind, with the ability to scale-up as the need rises.
If you know Python you may have learned of a very common study tool called Jupyter Notebook. It is highly demonstrative, it supports various add-ons and instruments. But there is always something needs to be upgraded. Often, sharing and working in the same file is not simple because libraries should be properly installed, language dependencies should be handled, etc.. Google has therefore suggested CoLaboratory-Jupyter on a Google Drive. Colaboratory, or Colab, in short, is Python’s online code editor and compiler.
As has been said, developers have a variety of tools to enhance their work. Nonetheless, most IT professionals involved in a real project will suggest that work starts from the research. Google has eased the process to get your hands on them by offering the following tools:
1. Google Datasets
The fundamental problem with every machine-learning model is to train it with the correct data. By providing data sets, Google Datasets addresses the issue.
Google Datasets is a series of data sets collected by Google, which are regularly updated by evaluating the researchers ‘ wide range of interests.
Google provides quite a wide range of types of data sets covering images, audios transcribed, videos and text. Targeted at a wide variety of users with different usage-cases, each category features a thorough run-down of the dataset with easy access to download links.
Users will prepare their models for real-world scenarios once they download the datasets and train their model on the data sets. You can use the Google Dataset Tool to search for more datasets.
2. Google Dataset Search
An issue case can sometimes be non-typical. Or, for example, simple datasets aren’t completely good for the purposes requested. Google does have something useful even in this situation. Dataset search allows you to find the most important and big datasets for nearly every task or query.
Results are sorted in the order of importance (more popular pages, e.g. Kaggle, will be at the top), links correspond exactly to the definition and download. Please note that this search engine is a beta-version right now.
Another effort by Google to improve the accuracy of its datasets by posing fun challenges to users, by asking them to identify different categories of images such as sketches, documents, articles, diagrams, and much more.
Google Crowdsource is a free app that Google has created in order to get more user feedback into its services.
Google’s algorithms are not as advanced as the human mind for certain things, such as image recognition, and there are plenty of ways for users to make certain Google products even better.
After download, you are asked by Crowdsource to pick the languages you fluent in. The software then displays five different panels that you can add to about Google resources.
These are transcription of images, recognition of handwriting, translation, validity of translation, and validation of map translation. If you have that competitive spirit, you’ll be awarded a fun badge and given achievements to reach until you start contributing.
1. Cloud TPU
The main goals of commercial products are increasing speed and decreasing of local resources. Here is the place for a Google Cloud. All tools in this category will be under this domain. So we will start from the dramatic computing ‘booster’ – Cloud TPU (tensor processing unit).
In simple language, it is a way to complete some large code computations with significant performance growth. Google uses this tool by itself in some of the most popular company products: Calendar, Gmail, etc.
There are several Tensor Processing Unit versions (different in the power and pricing) such as,
- Cloud TPU v2
- Cloud TPU v3
- Cloud TPU v2 Pod
- Cloud TPU v3 Pod
So companies can scale their projects as large and powerful as it may be needed.
2. Cloud AI
Rather than increasing the productivity, this method is about artificial intelligence. Design is similar enough to ML Kit: several off – the-shelf approaches can be used or something special can be suggested. Yet Cloud AI operates in large systems (not just within mobile applications) instead of the second. This allows interaction with more advanced technologies, not just simple ML solutions.
- AI Hub: offers a range of ready-to-use AI modules with sharing and testing options for the models.
- AI Building Blocks: incorporate custom applications with vision, voice, interaction and structured data. Such building blocks cover a wide range of cases and needs common for use. There are AI suggestions, for example, which helps to create sophisticated consulting programs based on consumer desires. The tool is ready out-of-box and just needs to be set on a company’s product.
- AI Platform: dedicated to the direct development of’ thinking’ computer systems, instead of previous components. In this case, engineers can create their own systems in portable pipelines (via Kubeflow), which could also be used on the Google Cloud Platform. This framework includes remote computation and the ability to train new models with no major code changes. It supports several Google applications for integration of a full project: Big Query, Google Cloud Platform, Deep Learning VM Imagee. There is an opportunity to share the model via AI Hub after the successful build-up.
3. Cloud AutoML
Cloud AutoML is a suite of machine learning products which allows developers with limited machine learning expertise to train high-quality, business-specific models. It relies on state-of – the-art transfer learning technologies from Google, and quest for neural architecture.
AutoML provides you with the power of machine learning even if you have minimal master learning knowledge. You can use AutoML to build on the machine learning capabilities of Google to develop your own custom machine learning models, customized to your business needs, and then incorporate those models into your applications and websites.
Whoever you are (or want to be): developer, researcher the commercial worker, anybody could profit from anything. These products cover a greater part of industrial and research activities as well as use cases.
The major benefit of the Google AI stack is that all methods are directly integrated among themselves. For example, this means that data could be stored in the Big Query, processed through a custom TF model, boosted by TPUs, and shared via AI Hub.
Those who are interested in the ML could open something new and interesting from Google’s expert systems resources day by day. It may not only help them improve the creation of software and data storage, but also build a more accurate and quicker Machine Learning models.
To explore more, please visit Google Tools!