OAD VS VevestaX

Compare OAD vs VevestaX and see what are their differences.

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OAD VevestaX
1 10
118 27
3.4% -
6.1 0.0
12 days ago over 1 year ago
Jupyter Notebook Jupyter Notebook
GNU General Public License v3.0 only 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.

OAD

Posts with mentions or reviews of OAD. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-03-01.

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 OAD and VevestaX you can also consider the following projects:

oemer - End-to-end Optical Music Recognition (OMR) system. Transcribe phone-taken music sheet image into MusicXML, which can be edited and converted to MIDI.

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

PUMA - 3D Printed Microscope

MLOps - End to End toy example of MLOps

BioImager - A .NET microscopy imaging application based on Bio library. Supports various microscopes by using imported libraries & GUI automation. Supports XInput game controllers to move stage, take images, run ImageJ macros on images or Bio C# scripts.

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-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.

mlflow-easyauth - Deploy MLflow with HTTP basic authentication using Docker

recommenders - Best Practices on Recommendation Systems

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

feast - Feature Store for Machine Learning