If your company wants to dive into artificial intelligence (AI) but finds it difficult to attract capable machine learning (ML) engineers, then you may want to try out Google’s free Kubeflow Pipelines software that allows the development of machine learning models in Kubernetes containers. Use of Kubeflow Pipelines will allow companies to dip their toes into AI and machine learning without sacrificing their ability to pivot if the venture proves to be unsuccessful.
Khari Johnson filed this report in VentureBeat:
Google Cloud senior director of product management Rajen Sheth said he agrees with estimates that there are only a few thousand machine learning engineers in the world with the ability to take deep learning from concept to production, but there are millions of data scientists and tens of millions of developers. Kubeflow Pipelines was designed to deal with that gap, empowering more data scientists and developers and helping businesses overcome the obstacles to becoming AI-first 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 layer, so different parts of the machine learning journey can be snapped together like Legos, Sheth said. This approach allows different members of a team to do things like label data, convert that data into features, and validate data. It can also come in handy for testing several iterations and replacing a model or approach if a better one is found.
