Rise of the machine learning engineer
Kubeflow, a machine learning toolkit for Kubernetes, announced the launch of version 1.0 of its platform. Kubeflow’s first major release marks a massive shift toward optimizing and advancing the growing field of machine learning engineering.
What is it? Kubeflow is an all-in-one tool for full-stack machine learning engineering powered by Kubernetes.
With Kubeflow, engineers and data scientists can develop, build, train, and deploy machine learning models. Kubeflow simplifies and intertwines many of these workflows—turning data science from a mishmash of tools into a robust set of workflows that more closely resembles common development practices.
Developers can access a central dashboard to manage notebooks, docker images, and more through a user interface. Kubeflow directly integrates hosted Jupyter notebooks, a CLI for deployment and upgrades, and a profile controller for multi-user team management.
Why you should pay attention: Originally open sourced in late 2017, Kubeflow has expanded rapidly with contributions from more than 30 organizations, including industry heavyweights like Alibaba Cloud, Ant Financial, AWS, Google, and Microsoft.
Kubeflow is helping to power the rise of machine learning engineers. That means data science workflows will be more in line with existing developer workflows—using containers, CLIs, pipelines, YAML configuration files, and more.
What’s next: The Kubeflow team is planning to roll out new pipelines for defining complex machine learning workflows, additional metadata for tracking jobs and models, and more.
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