Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! More often than not, when organizations deploy applications across hybrid and multicloud environments, they use the open-source Kubernetes container orchestration system. Kubernetes itself helps to schedule and manage distributed virtual compute resources and isn't optimized by default for any one particular type of workload, that's where projects like Kubeflow come into play. For organizations looking to run machine learning (ML) in the cloud, a group of companies including Google, Red Hat and Cisco helped to found the Kubeflow open-source project in 2017.

Arrikto, the leader in machine learning on Kubernetes, participated in the announcement of Kubeflow 1.5, the latest version of the open source MLOps platform, with contributions from Google, Arrikto, IBM, Twitter and Rakuten, alongside numerous other contributors. Kubeflow 1.5 delivers lower infrastructure costs, and helps simplify the operation of the end-to-end machine learning platform. Originally developed by Google, Kubeflow is a complete MLOps toolkit, including integrated components for model development, model training, multi-step pipelines, AutoML, serving, monitoring, artifact management, and experiment tracking. Running production machine learning workflows at scale is notoriously expensive due to outsized requirements on CPUs, GPUs, storage, and memory. Kubeflow 1.5 introduces several key features to reduce these costs.

KFServing enables serverless inferencing on Kubernetes and provides performant, high abstraction interfaces for common machine learning (ML) frameworks like TensorFlow, XGBoost, scikit-learn, PyTorch, and ONNX to solve production model serving use cases. Provide a Kubernetes Custom Resource Definition for serving ML models on arbitrary frameworks. Encapsulate the complexity of autoscaling, networking, health checking, and server configuration to bring cutting edge serving features like GPU autoscaling, scale to zero, and canary rollouts to your ML deployments. Our strong community contributions help KFServing to grow. We have a Technical Steering Committee driven by Bloomberg, IBM Cloud, Seldon, Amazon Web Services (AWS) and NVIDIA.

Kubeflow is a popular open-source Machine Learning platform that runs on Kubernetes. Kubeflow streamlines many valuable ML workflows i.e. it allows users to easily deploy development environments, scalable ML workflows with Kubeflow Pipelines, automated hyper-parameter tuning and neural architecture search with Katib, easy collaboration within teams and much more. With a such a large number of features also comes complexity. A full Kubeflow deployment contains many services and dependencies, making it difficult for users to customize, manage and install Kubeflow using the legacy kfctl CLI tool and a KfDef YAML file. For this reason, the upcoming Kubeflow 1.3 release has stopped using kfctl and instead is using standard Kustomize, making it easier to deploy Kubeflow with GitOps tools such as Argo CD.

Kubeflow, Google's solution for deploying machine learning stacks on Kubernetes, is now available as an official 1.0 release. Kubeflow was built to address two major issues with machine learning projects: the need for integrated, end-to-end workflows, and the need to make deploments of machine learning systems simple, manageable, and scalable. Kubeflow allows data scientists to build machine learning workflows on Kubernetes and to deploy, manage, and scale machine learning models in production without learning the intricacies of Kubernetes or its components. Kubeflow is designed to manage every phase of a machine learning project: writing the code, building the containers, allocating the Kubernetes resources to run them, training the models, and serving predictions from those models. The Kubeflow 1.0 release provides tools, such as Jupyter notebooks for working with data experiments and a web-based dashboard UI for general oversight, to help with each phase.

Google started the open-source Kubeflow Project with the goal of making Kubernetes the best way to run machine learning (ML) workloads in production. Today, Kubeflow 1.0 was released. Kubeflow helps companies standardize on a common infrastructure across software development and machine learning, leveraging open-source data science and cloud-native ecosystems for every step of the machine learning lifecycle. With the support of a robust contributor community, Kubeflow provides a Kubernetes-native platform for developing, orchestrating, deploying, and running scalable and portable ML workloads. Using Kubeflow on Google Cloud's Anthos platform lets teams run these machine-learning workflows in hybrid and multi-cloud environments while taking advantage of Google Kubernetes Engine's (GKE) enterprise-grade security, autoscaling, logging, and identity features.

For developers looking to more easily parallelize (and more) their machine learning (ML) workloads using Kubernetes, the open source project Kubeflow has reached version 1.0 this week. The now production-ready offers "a core set of stable applications needed to develop, build, train, and deploy models on Kubernetes efficiently." The project was first open sourced in December 2017 at KubeCon CloudNativeCon and has since grown to hundreds of contributors from more than 30 participating organizations such as Google, Cisco, IBM, Microsoft, Red Hat, Amazon Web Services and Alibaba. Alongside the blog post from the Kubeflow team itself, Google has offered a post on how Kubeflow works with Anthos, while IBM's Animesh Singh explores the "highlights of the work where we collaborated with the Kubeflow community leading toward an enterprise-grade Kubeflow 1.0." In an interview with The New Stack, Singh explained the origins of Kubeflow as one attempting to simply bring TensorFlow to Kubernetes.

A stable version of an automation tool released this week aims to make life easier machine learning developers training and scaling models, then deploying ML workloads atop Kubernetes clusters. Roughly two years after its open source release, Kubeflow 1.0 leverages the de facto standard cluster orchestrator to aid data scientists and ML developers in tapping cloud resources to run those workloads in production. Among the stable workflow applications released on Monday (March 2) are a central dashboard, Jupyter notebook controller and web application along with TensorFlow and PyTorch operators for distributed training. Contributors from Google, IBM, Cisco Systems, Microsoft and data management specialist Arrikto said Jupyter notebooks can be used to streamline model development. Other tools can then be used to build application containers and leverage Kubernetes resources to train models.

Machine learning must address a daunting breadth of functionalities around building, training, serving, and managing models. Doing so in a consistent, composable, portable, and scalable manner is hard. The Kubernetes framework is well suited to address these issues, which is why it's a great foundation for deploying machine learning workloads. The Kubeflow project's development has been a journey to realize this promise, and we are excited that journey has reached its first major destination – Kubeflow 1.0. Always ready to work with a strong and diverse community, IBM joined this Kubeflow journey early on.

On behalf of the entire community, we are proud to announce Kubeflow 1.0, our first major release. Kubeflow was open sourced at Kubecon USA in December 2017, and during the last two years the Kubeflow Project has grown beyond our wildest expectations. There are now hundreds of contributors from over 30 participating organizations. Kubeflow's goal is to make it easy for machine learning (ML) engineers and data scientists to leverage cloud assets (public or on-premise) for ML workloads. You can use Kubeflow on any Kubernetes-conformant cluster.