Let’s face a simple truth: nowadays, cognitive tech is not an option or nice perk—it’s a must-have. Companies invest in cognitive technologies to boost their competitiveness. Companies using artificial intelligence can open a narrow lead over competitors in the short-term, and often outpace them in the long-term.
According to respondents to Deloitte’s survey, a major benefit of AI is enhancing products and processes. A company that uses AI can optimize its workflow, make more informed decisions, and liberate employees, encouraging creativity. Most cognitive technologies are built on machine learning, and many use deep learning networks, a more complex ML approach. Advancements in natural language processing improve voice recognition, intelligent assistants, and chatbots.
According to Business Insider data, the chatbot market is expected to grow 29.7% annually and reach $9.4 billion by 2024. To meet the growing demand for innovative solutions, Google Cloud launched Google AI Hub, a specialized framework to accelerate enterprise AI projects. Google AI Hub’s mission is to make AI available to everyone and remove barriers to adopting AI.
“Managing intellectual capital and transferring knowledge across markets and industries is a core competitive advantage of the Global Wunderman Thomas Analytics team. With hundreds of data scientists, engineers, architects, and analysts across the globe alongside a diverse list of clients and requirements, the sharing of intelligence is vital. AI Hub is a platform that lets us centralize our code and knowledge in a way that can step up the pace of deployment and learnings globally, giving us the scale to deliver data-driven marketing excellence.”
— Yannis Kotziagkiaouridis, Global Chief Analytics Officer, Wunderman
Google AI Hub Tools
Basically, Google’s AI Hub is a one-stop shop for all things related to artificial intelligence. It hosts a repository of ready-to-use components, such as holistic AI pipelines and custom-tailored algorithms. AI Hub enables businesses to host their own AI content and privately leverage Google’s sharing capabilities. Companies can share content seamlessly across their organization, explore and use components developed by other teams and organizations, and tweak publicly available components according to the company’s unique business needs.
“Our goal is to put AI in reach of all businesses. But doing that means lowering the barriers to entry. That’s why we build all our AI offerings with three ideas in mind: make them simple, so more enterprises can adopt them, make them useful to the widest range of organizations, and make them fast, so businesses can iterate and succeed more quickly.”
— Hussein Mehanna, Engineering Director, Google Cloud ML Platform
A company can access public and proprietary content, with the latter shared securely among authorized users only. The public assets definition pertains to a huge library that encompasses myriads of research papers and machine learning frameworks—Kaggle, DeepMind, and much more.
Useful AI Services
Google is a leading investor in machine learning and artificial intelligence, specifically frameworks for developing sophisticated models. Google released TensorFlow in 2015 and Kubeflow in 2018, and companies have explored and applied these tools extensively since then. Google had great foresight to develop and collect its ML assets under one roof and make a unified space for ML services. Imagine having a well-organized workstation with everything you need to work effectively and drive results within reach from your chair. This is exactly what Google Cloud AI Hub is—a comprehensive toolkit for preparing data, and collaborating on and sharing ML models.
Let’s walk through what Google’s AI Platform has to offer:
A catalog of models that can be used in APIs powered by Google or other popular frameworks such as Kubeflow, Jupyter Notebooks, etc. Hub’s distinctive benefit is clarity: each model developed in AI Hub is packed and tagged so finding what one needs won’t be challenging.
Deep Learning Virtual Machines (VMs)
These are used to instantiate virtual machine images which include the most commonly used ML mechanisms. They are installed in GPU (Graphics Processing Unit) and TPU (Tensor Processing Unit) software. Deep Learning VMs run the most up-to-date frameworks, are optimized with NVIDIA® CUDA-X AI libraries, provide an easy notebook experience, and allow for quick prototyping.
Creates VM instances pre-packaged with JupyterLab and management tools. Notebooks are automatically loaded with the essential packages for TensorFlow and PyTorch environments.
This toolkit simplifies and accelerates deploying top-notch public ML systems targeted at various infrastructures. Companies use Kubeflow Pipelines to develop and deploy portable, scalable machine ML workflows. It assists in collaboration, reproducibility, and visualization of ML workflows and lifecycles.
“Google Cloud's AI Hub is a hosted repository of plug-and-play AI components, including end-to-end AI pipelines and out-of-the-box algorithms. AI Hub provides enterprise-grade sharing capabilities that let organizations privately host their AI content to foster reuse and collaboration among machine learning developers and users internally. You can also easily deploy unique Google Cloud AI and Google AI technologies for experimentation and ultimately production on Google Cloud and hybrid infrastructures.”
— AI Hub
Tensorflow deep learning framework
In the eyes of many developers, Tensorflow is the most useful deep learning framework. Using Tensorflow, complex and accurate apps with videos, texts, images, or audio can be built easily. Even more importantly, Tensorflow’s library can be run on all available devices.
AutoML natural language
This tool helps developers build and deploy ML models used for natural language processing of documents, and subsequent assessment, categorization, and attitude scoring of those documents. Simply put, ML is used to understand the structure and meaning of a document, and extract information on the author’s sentiment.
“Data variability, however, is still an important factor in any machine learning-based solution. AutoML automatically solves a lot of basic problems for variance in data, making it possible for you to use as little as a few thousand images to train a custom model.”
— Nitin Aggarwal, Technical Program Manager, Google Cloud
Cloud Speech API
Communication requires two parties—one that speaks and one that listens—and there are two kinds of Cloud Speech APIs. The text-to-speech (TTS) API is used to convert text into spoken word in 40 languages and approximately 200 voices. The speech-to-text (STT) API is used to transcribe speech, and supports 125 languages. TTS is the speaker, and STT is the listener. The APIs complement each other, and both support popular programming languages such as Python, Java, etc.
Cloud Video intelligence
The Video Intelligence API allows developers to interface with and use Google’s video analysis technology. It has pre-trained ML models that can recognize an array of different objects, locations, and actions in stored or streamed videos. The API is regularly updated with new concepts and capabilities.
“We want to use AI to augment the abilities of people, to enable us to accomplish more and to allow us to spend more time on our creative endeavors.”
— Jeff Dean, Google Senior Fellow
How to Publish a Notebook on AI Hub — easy-peasy
“AI Hub’s ease of use puts Google at the forefront of enterprise AI implementation. You can only deploy a powerful tool effectively when you know exactly what to do with it. Sharing and uploading assets into AI Hub is a big responsibility and any employee within an organization who does so must consider corporate policies before leveraging resources.”
— Vlad Medvedovsky, Founder and Chief Executive Officer at Proxet, a custom software development solutions company.
Here are the steps to upload Notebook on AI Hub:
- Visit AI Hub and sign in with the account attached to your company
- You will be offered the choice of many Notebooks. Each Notebook has a form containing all the information on that particular Notebook. Choose the one you need from the list.
- Provide metadata, i.e. name the Notebook and specify the type of input data. Specifying the machine learning workflow, technique, use case, and labels is optional.
- Fill in the “Version” section.
- Finalize the uploading procedure in the “Review and Finish” section.
- Click “Publish.”
Google AI Hub is another successful step in democratizing AI and making it more accessible. Promoting collaboration, AI Hub makes it possible for developers, researchers, and partners to work in synergy, addressing enterprise AI issues together. Proxet is a Machine Learning company, experienced in every subspecialty of this large and complex domain. We use a vast set of techniques and solutions to add value to our client’s business and make the best of innovative technologies.
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