Google introduces AI Hub and Kubeflow

Google is probably the most advanced company in the world right now when it comes to Artificial Intelligence (AI) development, with some of their products surpassing human capabilities. However, AI is not readily available to every business out there. The tech giant is trying to change that with the introduction of the Google AI Hub.

These AI offerings are designed with three ideas in mind: make it simple, so as to make the adoption process easier, expand the number of ways it can be used, so an organization can use it easily, and make AI extremely fast, helping the business succeed quickly.

In the first half of 2018, Google announced AutoML to help firms with limited knowledge in Machine Learning (ML) to build their customized models. They have also invested in developing specialized training and certifications to help increase people ML skills rapidly.

The introduction of AI Hub

There is a general lack of knowledge when it comes to the development of resources in Machine Learning. Google's AI Hub plans on changing it completely. It provides plug and play ML content, including pipelines, Jupyter notebooks, TensorFlow modules, and more.

This is bound to offer benefits such as the development of high-quality resources by Google Cloud AI and Google Research. The second one is mainly for enterprises who want access to a private and secure hub where they can easily share their resources, making it extremely easy for them to use them in GCP or on various hybrid infrastructures which make use of the Kubeflow Pipeline System.

Kubeflow Pipelines, which is a component of Kubeflow, takes advantage of the company's advanced in-house developed TensorFlow Extended libraries, which provide a solution to problems such as model analysis data validation, training-serving skew, data drift, and more.

There are various scenarios in which AI is actively helping corporations help save valuable time. For example, Meredith Corporation, a media company, uses machine learning to automate content classification, applying a custom universal taxonomy with Cloud AutoML and Natural Language. Machine learning helps them make content classification more repeatable and scalable, saving time and improving reader experiences.

“At Meredith Corporation, we’re focused on creating compelling content across platforms and life stages for brands such as PEOPLE, Better Homes & Gardens, Martha Stewart Living, Allrecipes, and Food & Wine,” says Alysia Borsa, Chief Marketing & Data Officer, Meredith Corporation. “By using Natural Language and AutoML services to apply our custom universal taxonomy to our content, we’re able to better identify and respond to emerging trends, enable robust detailed targeting and provide our audience with more relevant and engaging experiences.”

Of course, ML and AI are the two prominent technologies which will help shape the future and help us understand and solve complex problems which we thought we pretty much unsolvable until now.
Google is taking one step at a time, helping every type of business gain access to these tools.