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Google AI Tools

Best AI and machine learning tools, infrastructure offered by Google for data scientists, developers

Tools are the most essential things to succeed at any task. For a photographer to be successful, there is a need for vision and also a great camera, their tool. Similarly, in the field of artificial intelligence (AI) also, there is a need for developers, engineers, machine learning engineers, to gain access to the best tools.

AI developers need access to tools like a rich quality data set, a ML program capable of training faster, and a cloud service able to support the work. One such cloud platform is Google Cloud and it offers a variety of AI tools with the best of Google’s research and technology. Gartner has named Google Cloud leader in the 2021 Magic Quadrant for Cloud AI developer services.

When it comes to all the AI tools offered by Google, they are essentially classified in terms of data science, AI infrastructure, and responsible AI. Here is a look at some of the tools offered by the search giant that aims to help AI developers solve problems without worrying about speed or efficiency.

Vertex AI

The first AI tool from Google causing maximum impact among developers is called Vertex AI. It is an unified machine learning platform developed with data scientists in mind. The tool allows data scientists to build, deploy, and scale more effective AI models. It is ideal for accelerating data preparation, one of the key tasks that can set developers behind.

Vertex AI is also good for scaling data, training and experimentation, and model deployment. Google also offers Vertex AI Workbench as a single development environment for the entire data science workflow. With Vertex AI and Vertex AI Workbench, Google is offering a tool design with data scientists in mind.


AutoML is among the fastest growing subfields of machine learning that automates time-consuming, iterative tasks in ML model development. As part of the AI for Developers tools package, Google Cloud offers AutoML that allows developers to train high-quality custom ML models with minimal effort and machine learning expertise.

With AutoML, AI and ML developers will be able to build their custom machine learning models in minutes. The tool can also be used by developers to train models specific to their business needs.

Cloud Inference API

One of the problems facing businesses with access to data is getting insights and Cloud Inference API aims to solve that. With Cloud Inference API, AI developers can uncover insights from large scale, typed time-series data.

It also aids with indexing and loading a dataset consisting of multiple stored data sources. Since it is cloud-based, the API is able to execute inference queries over loaded datasets and supports developers with unloading or cancelling the loading of a dataset.

Cloud Natural Language and Dialogflow

With the Natural Language API, AI developers can apply natural language understanding to apps. They can train their open ML models to classify, extract, and detect sentiment. The tool can also be used to derive insights from unstructured text using Google’s own machine learning. Dialogflow allows developers to create natural interaction for complex multi-turn conversations, build and deploy advanced agents quickly, and build enterprise grade scalability.

Media Translation (Beta)

The Media Translation, though in beta, is among the most useful AI tools offered as part of Google Cloud’s AI and machine learning products. It allows developers to add real-time audio translation to their content and applications. The service is already used by Google for one of the features available on the Translate app.

It delivers real-time speech translation directly from the source audio data. Google also offers speech-to-text service to power automatic speech recognition and real-time transcription, while text-to-speech uses Google’s AI technologies to turn any text into natural-sounding speech.

Translation AI

Every business and organisation wants to support every language spoken by their customers and one of the challenges is making their product multilingual. The Translation AI from Google helps solve that for content and apps with its fast, dynamic machine translation.

The service delivers a seamless user experience with real-time translation. It also makes it easier for businesses to reach global markets through internationalisation of their products and makes the content engaging for the local audience.

Video AI and Vision AI

Video AI tool from Google helps with content discovery and extracting rich metadata at the video, shot, or frame level. It also creates custom entity labels with AutoML Video Intelligence. Vision AI, on the other hand, helps with deriving insights from images in the cloud or at the edge with AutoML Vision or using any of the pre-trained Vision API models.

The AutoML Vision can be used to detect emotion, understand text, among other key attributes associated with images. The same tool can also be used to train ML models to classify images by custom labels. This is one of the most widely used AI services right now as companies become prepared for creating well labelled data.

Deep Learning Containers and Deep Learning VM Image

Google does not just stop at offering AI tools for data scientists and developers but also offers an expansive AI infrastructure. It starts with Deep Learning Containers that are preconfigured and optimised for deep learning environments, which is ideal for prototyping AI applications. There is also Deep Learning VM Image which helps accelerate model training and deployment.


One of the requirements of AI developers and data scientists is access to high performance computing. With Google Cloud, developers can access high-performance GPUs for machine learning, scientific computing, and 3D visualisation. With a high-performance GPU based cloud, you can speed up compute jobs like machine learning and even accelerate specific workloads on your virtual machines.

TensorFlow Enterprise

While Google Cloud as a cloud service offers a number of benefits to AI developers, there is also TensorFlow Enterprise that acts as a managed service with enterprise-grade support for reliability and performance.

This AI infrastructure can be used to boost enterprise development with long-term support on specific distributions, scaling resources across CPUs, GPU, and cloud TPUs. It also supports developing and deploying TensorFlow across managed services.


Tensor Processing Units (TPUs) are Google’s custom-developed application-specific integrated circuits (ASICs) used to accelerate machine learning workloads. It is used to run cutting-edge machine learning models with AI services on Google Cloud. The tool also allows iterating quickly and frequently on machine learning solutions. With TPUs, AI developers can build their own ML-powered solutions for real-world use cases.

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2048 1472 Editorial Staff
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