VevestaX VS bodywork-pymc3-project

Compare VevestaX vs bodywork-pymc3-project and see what are their differences.

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VevestaX bodywork-pymc3-project
10 1
27 13
- -
0.0 5.3
over 1 year ago almost 2 years ago
Jupyter Notebook Jupyter Notebook
Apache License 2.0 MIT License
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.

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.

bodywork-pymc3-project

Posts with mentions or reviews of bodywork-pymc3-project. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-05-17.

What are some alternatives?

When comparing VevestaX and bodywork-pymc3-project 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

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

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

amazon-sagemaker-examples - Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

feast - Feature Store for Machine Learning

strava-analysis - 🏃📊 Using strava to do personal analyses and to practice data scientist skills.