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. Part one..

For Organizations

  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.

  1. 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.

Conclusion


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!

https://ai.google/tools/

By Hansika Ekanayake