See how you can build a ML pipeline with Kubeflow! After setting up Kubeflow on your Kubernetes Cluster you (and your data science team) can explore the dataset and develop the first version of the ML model.
Accelerating your cloud native journey with Intersight Kubernetes Service
Delivering on-premise ML capabilities, with potential hybrid configurations giving data scientists the best of both worlds.
Do you know that Kubeflow 1.0 has just been recently released by the Kubeflow Community? Kubeflow is the open source project integrating TensorFlow, PyTorch and other machine learning capabilities into a cohesive tool, from data ingestion, through machine learning, and even inferencing.
Kubeflow 1.0 is ready. Through perseverance and hard work of some talented individuals and close collaboration across several organizations, together we have achieved a pivotal milestone for the community. In this article we would like to take a step back, celebrate the success, and discuss some of the steps we need to take the project to the next level.
Kubernetes has been a catalyst in building AI/ML capabilities, however DIY setting up and maintaining such as stack can be complex. As more and more Cisco Container Platform, customers are interested in machine learning, we are continuously evolving the support to enable them in their journey.
The Kubeflow project has made a tremendous progress, and it is awesome to be recognized as Google Cloud Technology Partner of the Year in the Container category for a second year.
The field of machine learning is progressing at a break-neck speed. New algorithms and techniques are being published at such a high frequency that it is impractical to keep pace.
Data is already a first-class citizen in many enterprises. Today, data is used to derive insights, influence decisions, automate and optimize operational efficiency.
