Kubeflow solves many of the challenges involved in orchestrating machine learning pipelines by providing a set of tools and APIs that simplify the process of training and deploying ML models at scale. Kubeflow is able to accommodate the needs of multiple teams in one project and allows those teams to work from any infrastructure. This means that data scientists can train and serve ML models from the cloud of their choice.
Overall, Kubeflow can help standardize machine learning operations (MLOPs) by organizing projects while leveraging the power of cloud computing. Some of the key use cases for Kubeflow include data preparation, model training, evaluation, optimization, and deployment.
Kubernetes is key to accelerating the ML lifecycle as these technologies provide data scientists the much needed agility, flexibility, portability, and scalability to train, test, and deploy ML models.
Scalability: Kubernetes allows users to scale ML workloads up or down, depending on demand. This ensures that machine learning pipelines can accommodate large-scale processing and training without interfering with other elements of the project.
Efficiency: Kubernetes optimizes resource allocation by scheduling workloads onto nodes based on their availability and capacity. By ensuring that computing resources are being utilized with intention, users can expect a reduction in cost and an increase in performance.
Portability: Kubernetes provides a standardized, platform-agnostic environment that allows data scientists to develop one ML pipeline and deploy it across multiple environments and cloud platforms. This means not having to worry about compatibility issues and vendor lock-in.
Fault tolerance: With built-in fault tolerance and self-healing capabilities, users can trust Kubernetes to keep ML pipelines running even in the event of a hardware or software failure.
Red Hat® OpenShift® is the industry's leading containers and Kubernetes hybrid cloud platform. With DevOps capabilities (e.g. OpenShift Pipelines, OpenShift GitOps, and Red Hat Quay) and integration with hardware accelerators, Red Hat OpenShift enables better collaboration, and accelerates the delivery of AI-powered applications.
With Red Hat OpenShift, organizations can automate and simplify the iterative process of integrating ML models into software development processes, production rollout, monitoring, retraining, and redeployment for continued prediction accuracy.