Google Cloud introduced Kubeflow Pipeline to temporarily partner within commerce and for more democratizing access to Artificial Intelligence (AI). Kubeflow Pipelines is freely accessible and is getting open-sourced.

Rajen Sheth, senior director of product management of Google Cloud, claimed that the estimates are true about being only a several thousand machine learning engineers globally with the skill to make deep learning reach production stage from a mere idea, but there are lots of data scientists and even more of developers.

Kubeflow Pipelines was modeled to remove the gap; allowing more data scientists and developers and assisting commerce to remove the hurdles in being the first AI companies.

“One of the biggest problems we’re seeing right now is companies are now trying to build up teams of data scientists, but it’s such a scarce resource that unless that’s utilized well, it starts to get wasted,” Sheth said. “One observation we’ve seen is that in probably over 60 percent of cases, models are never deployed to production right now. So we’re building a number of things to hopefully help cure that.”

Pipelines is a composable cover, making several parts of the machine learning trip snap together like Legos, Sheth claimed.

“They can just swap in the new model, keep the rest of the pipeline in place, and then see: ‘Does that new model help the output significantly?’ So it enables … rapid experimentation in a much better way,” he said.

“What we are doing with Pipelines, it can start to involve developers, it can start to involve business analysts, it can start to involve end users such that they can become part of this team that can build a Pipeline.”

Kubeflow is basically an open source plan launched earlier this year by Google. It is for machine learning with Kubernetes containers. With Kuberneter, commerce will be able to be flexible and elude from training AI models using the on-premise platforms and raw data or training them in the cloud.

Google also released AI Hub in alpha, which develops on top of TensorFlow Hub, a machine learning hub that got released earlier this year. AI Hub is modeled to be everything for people who want to deploy or train AI models.

Additionally, AI Hub will get the resources from Google, like content from Kaggle – a platform of over 2 million data scientists – and famous TensorFlow embeddings. With time, Google wishes AI Hub to turn into a room for famous models created by the bigger ecosystem.

“We eventually want AI Hub to be a place where third parties can also share information and turn it more into a marketplace over time,” Sheth said. “What we’re finding is that community could actually solve the problems of many of our customers.”

In the start, AI Hub will be accessible by about 100 business partners.

AI Hub and Kubeflow Pipelines have the objective of teaching workforces to break down the walls between groups within the firms, making the efforts of ML engineers, developers and data scientists more appreciable.