mlflow-easyauth VS VevestaX

Compare mlflow-easyauth vs VevestaX and see what are their differences.

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mlflow-easyauth VevestaX
1 10
100 27
- -
3.0 0.0
8 months ago over 1 year ago
Shell Jupyter Notebook
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.

mlflow-easyauth

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

We haven't tracked posts mentioning mlflow-easyauth yet.
Tracking mentions began in Dec 2020.

VevestaX

Posts with mentions or reviews of VevestaX. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-05-12.

What are some alternatives?

When comparing mlflow-easyauth and VevestaX you can also consider the following projects:

bodywork-pipeline-with-aporia-monitoring - Integrating Aporia ML model monitoring into a Bodywork serving pipeline.

MLOps - End to End toy example of MLOps

create-react-app-buildpack - ⚛️ Heroku Buildpack for create-react-app: static hosting for React.js web apps

vertex-ai-samples - Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud

Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.

mlflow-tracking-server - This repository hosts the code to make it easier to deploy a customizable and flexible MLflow tracking server solution to your Kubernetes cluster.

mlflow-deployments - Source code for the post Effortless deployments with MLFlow, showcasing how logging models using MLFLow can provide you want to easily deploy them in production later.

OAD - Collection of tools and scripts useful to automate microscopy workflows in ZEN Blue using Python and Open Application Development tools and AI tools.

recommenders - Best Practices on Recommendation Systems

bodywork-pymc3-project - Serving Uncertainty with Bayesian inference, using PyMC3 with Bodywork

flytesnacks - Flyte Documentation 📖

feast - Feature Store for Machine Learning