Software Alternatives, Accelerators & Startups

Kazuhm VS Metaflow

Compare Kazuhm VS Metaflow and see what are their differences

Kazuhm logo Kazuhm

Manage your containerized workloads through Kazuhm's easy to use distributed computing technology. Kazuhm saves cloud costs, improves security and latency.

Metaflow logo Metaflow

Framework for real-life data science; build, improve, and operate end-to-end workflows.
  • Kazuhm Landing page
    Landing page //
    2021-07-27

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.

  • Metaflow Landing page
    Landing page //
    2023-03-03

Kazuhm videos

No Kazuhm videos yet. You could help us improve this page by suggesting one.

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Metaflow videos

useR! 2020: End-to-end machine learning with Metaflow (S. Goyal, B. Galvin, J. Ge), tutorial

More videos:

  • Review - Screencast: Metaflow Sandbox Example

Category Popularity

0-100% (relative to Kazuhm and Metaflow)
DevOps Tools
39 39%
61% 61
Workflow Automation
22 22%
78% 78
Containers As A Service
100 100%
0% 0
Workflows
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Kazuhm and Metaflow

Kazuhm Reviews

We have no reviews of Kazuhm yet.
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Metaflow Reviews

Comparison of Python pipeline packages: Airflow, Luigi, Gokart, Metaflow, Kedro, PipelineX
Metaflow enables you to define your pipeline as a child class of FlowSpec that includes class methods with step decorators in Python code.
Source: medium.com

Social recommendations and mentions

Based on our record, Metaflow seems to be more popular. It has been mentiond 12 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.

Kazuhm mentions (0)

We have not tracked any mentions of Kazuhm yet. Tracking of Kazuhm recommendations started around Mar 2021.

Metaflow mentions (12)

  • What are some open-source ML pipeline managers that are easy to use?
    I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home. Source: about 1 year ago
  • Needs advice for choosing tools for my team. We use AWS.
    1) I've been looking into [Metaflow](https://metaflow.org/), which connects nicely to AWS, does a lot of heavy lifting for you, including scheduling. Source: about 1 year ago
  • Selfhosted chatGPT with local contente
    Even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf. Source: about 1 year ago
  • [OC] Gender diversity in Tech companies
    They had to figure out video compression that worked at the volume that they wanted to deliver. They had to build and maintain their own CDN to be able to have a always available and consistent viewing experience. Don’t even get me started on the resiliency tools like hystrix that they were kind enough to open source. I mean, they have their own fucking data science framework and they’re looking into using neural... Source: over 1 year ago
  • Going to Production with Github Actions, Metaflow and AWS SageMaker
    Github Actions, Metaflow and AWS SageMaker are awesome technologies by themselves however they are seldom used together in the same sentence, even less so in the same Machine Learning project. - Source: dev.to / almost 2 years ago
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What are some alternatives?

When comparing Kazuhm and Metaflow, you can also consider the following products

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.

Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.

Mesosphere DCOS - Mesosphere DCOS organizes your entire infrastructure as if it was a single computer.

Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.

DepHell - :package: :fire: Python project management. Manage packages: convert between formats, lock, install, resolve, isolate, test, build graph, show outdated, audit. Manage venvs, build package, bump ver...

Rancher - Open Source Platform for Running a Private Container Service