Kazuhm SaaS platform unifies the compute resources of an organization from desktops, to servers, to cloud, to edge, creating a private grid to place and process containerized workloads, optimize IT costs, security, and performance.
Through an easy user interface, customers leverage Kazuhm today to simplify Kubernetes and the deployment of popular data science applications, build their own private distributed compute networks, run workloads on-premises enabling the lowest possible latency, and easily manage multi-cloud and hybrid cloud environments.
Kubernetes-Made-Easy -- Set up and cluster deployment is super quick with container placement and host monitoring intuitively simple.
Multi-Cloud, Hybrid-Cloud Management -- Escape from vendor lock-in and centrally manage all your Public Cloud Hosts for FREE.
Data Science On Demand -- Simplify deployment of Spark and Jupyter and process workloads both on-premise and in the cloud.
Offset Cloud Costs -- Get “Cloud Smart”. Process containerized workloads on your Linux and Windows desktops and servers to offset cloud costs.
Low-Latency Workload Processing -- Reduce latency and improve performance by processing your data on-premise or at the edge – when milliseconds count.
Distributed Computing Anywhere -- Connect your desktops, both Windows and Linux, and servers or even your edge devices to create a powerful compute fabric.
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Based on our record, Kubernetes seems to be more popular. It has been mentiond 280 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Deploying AI models into production requires tools that can package applications and manage them at scale. Docker simplifies the deployment of AI applications by containerizing them, ensuring that the application runs smoothly in any environment. Kubernetes, an orchestration system for Docker containers, allows for the automated deployment, scaling, and management of containerized applications, essential for AI... - Source: dev.to / 2 days ago
To learn more, you can start by exploring the official Kubernetes documentation. - Source: dev.to / 16 days ago
This package is widely used for powerful CLI builds, it is used for example for Kubernetes CLI and GitHub CLI, in addition to offering some cool features such as automatic completion of shell, automatic recognition of flags (the tags) , and you can use -h or -help for example, among other facilities. - Source: dev.to / 20 days ago
"You are holding it wrong", huh? From the homepage https://kubernetes.io/: "Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications." Do you see "not recommended for smaller-scale applications" anywhere? Including on the entire home page? Looking for "small", "big" and "large" also yields nothing. - Source: Hacker News / 29 days ago
Open Source and Cloud Computing: A Match Made in Heaven The cloud is accelerating OSS adoption. Cloud-native technologies like Kubernetes [https://kubernetes.io/] and Istio [https://istio.io/], both open-source projects, are revolutionizing how applications are built and deployed across cloud platforms. - Source: dev.to / about 1 month ago
Activeeon - ProActive Workflows & Scheduling is a java-based cross-platform workflow scheduler and resource manager that is able to run workflow tasks in multiple languages and multiple environments: Windows, Linux, Mac, Unix, etc.
Rancher - Open Source Platform for Running a Private Container Service
Mesosphere DCOS - Mesosphere DCOS organizes your entire infrastructure as if it was a single computer.
Docker - Docker is an open platform that enables developers and system administrators to create distributed applications.
Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.
Helm.sh - The Kubernetes Package Manager