deployKF VS pipelines

Compare deployKF vs pipelines and see what are their differences.

deployKF

deployKF builds machine learning platforms on Kubernetes. We combine the best of Kubeflow, Airflow†, and MLflow† into a complete platform. (by deployKF)
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deployKF pipelines
2 2
275 3,457
13.5% 1.0%
8.6 9.8
9 days ago 5 days ago
Shell Python
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

deployKF

Posts with mentions or reviews of deployKF. We have used some of these posts to build our list of alternatives and similar projects.

pipelines

Posts with mentions or reviews of pipelines. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-03-31.
  • Putting an ML model into production using Feast and Kubeflow on Azure (Part I)
    2 projects | dev.to | 31 Mar 2021
    Kubeflow Pipelines comes with a pre-defined KFServing component which can be imported from the GitHub repo and reused across the pipelines without the need to define it every time. KFServing is Kubeflow's solution for "productionizing" your ML models and works with a lot of frameworks like Tensorflow, sci-kit, and PyTorch among others.
  • Machine Learning Orchestration on Kubernetes using Kubeflow
    5 projects | dev.to | 23 Mar 2021
    You can run the notebook from the dashboard and create the pipeline. Please note, in Kubeflow v1.2, there is an issue causing RBAC: permission denied error while connecting to the pipeline. This will be fixed in v1.3 and you can read more about the issue here. As a workaround, you need to create Istio ServiceRoleBinding and EnvoyFilter to add an identity in the header. Refer this gist for the patch.

What are some alternatives?

When comparing deployKF and pipelines you can also consider the following projects:

bigbang - BigBang the product

kubeflow - Machine Learning Toolkit for Kubernetes

local-gitops - An automated local cluster setup w/ tls, monitoring, ingress and DNS configuration.

fashion-mnist-kfp-lab - A notebook showing how to easily convert a current notebook you have to a notebook that can be run on Kubeflow Pipelines.

kubernetes-demo-gitops - This is the GitOps repo for project vjanz/kubernetes-demo-app

fashion-mnist - A MNIST-like fashion product database. Benchmark :point_down:

argocd-lovely-plugin - A plugin to make Argo CD behave like we'd like.

soopervisor - ☁️ Export Ploomber pipelines to Kubernetes (Argo), Airflow, AWS Batch, SLURM, and Kubeflow.

awesome-argo - A curated list of awesome projects and resources related to Argo (a CNCF graduated project)

community - Information about the Kubeflow community including proposals and governance information.

kserve - Standardized Serverless ML Inference Platform on Kubernetes

bodywork - ML pipeline orchestration and model deployments on Kubernetes.