AI/ML model training is becoming more time consuming due to the increase in data needed to achieve higher accuracy levels. This is compounded by growing business expectations to frequently re-train and tune models as new data is available.
The two combined is resulting in heavier compute demands for AI/ML applications. This trend is set to continue and is leading data center companies to prepare for greater compute and memory-intensive loads for AI.
Getting the right hardware and configuration can overcome these challenges.
In this webinar, you will learn:
- Kubeflow and AI workload automation
- System architecture optimized for AI/ML
- Choices to balance system architecture, budget, IT staff time and staff training.
- Software tools to support the chosen system architecture
With Charmed Kubeflow, deployment and operations of Kubeflow are easy for any scenario.
Charmed Kubeflow is a collection of Python operators that define integration of the apps inside Kubeflow, like katib or pipelines-ui.
Use Kubeflow on-prem, desktop, edge, public cloud and multi-cloud.
Kubeflow makes deployments of Machine Learning workflows on Kubernetes simple, portable and scalable.
Kubeflow is the machine learning toolkit for Kubernetes. It extends Kubernetes ability to run independent and configurable steps, with machine learning specific frameworks and libraries.
The Kubeflow project is dedicated to making deployments of machine learning workflows on Kubernetes simple,
portable and scalable.
You can install Kubeflow on your workstation, local server or public cloud VM. It is easy to install with MicroK8s on any of these environments and can be scaled to high-availability.