bodywork-pymc3-project
Serving Uncertainty with Bayesian inference, using PyMC3 with Bodywork (by bodywork-ml)
bodywork-pipeline-with-aporia-monitoring
Integrating Aporia ML model monitoring into a Bodywork serving pipeline. (by bodywork-ml)
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| bodywork-pymc3-project | bodywork-pipeline-with-aporia-monitoring | |
|---|---|---|
| 1 | 1 | |
| 13 | 4 | |
| - | - | |
| 5.3 | 0.0 | |
| almost 2 years ago | almost 2 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | 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.
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.
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.
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A tutorial on how to handle prediction uncertainty in production systems, by using Bayesian inference and probabilistic programs
All of the code is hosted in a GitHub repo, that you can use as a template for your own projects.
bodywork-pipeline-with-aporia-monitoring
Posts with mentions or reviews of bodywork-pipeline-with-aporia-monitoring.
We have used some of these posts to build our list of alternatives
and similar projects.
-
Calling Aporia from Bodywork Pipelines to Monitor Models in Production
Monitoring models for drift and degradation is not easy - theoretically or practically. In this example project we show to outsource these problems to Aporia’s model monitoring platform, by using their Python client from within a Bodywork pipeline.
What are some alternatives?
When comparing bodywork-pymc3-project and bodywork-pipeline-with-aporia-monitoring you can also consider the following projects:
VevestaX - 2 Lines of code to track ML experiments + EDA + check into Github
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
amazon-sagemaker-examples - Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
bodywork - ML pipeline orchestration and model deployments on Kubernetes.
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
indaba-pracs-2022 - Notebooks for the Practicals at the Deep Learning Indaba 2022.
ml-pipeline-engineering - Best practices for engineering ML pipelines.
bodywork-pymc3-project vs VevestaX
bodywork-pipeline-with-aporia-monitoring vs evidently
bodywork-pymc3-project vs amazon-sagemaker-examples
bodywork-pipeline-with-aporia-monitoring vs VevestaX
bodywork-pymc3-project vs bodywork
bodywork-pipeline-with-aporia-monitoring vs ML-Workspace
bodywork-pymc3-project vs indaba-pracs-2022
bodywork-pipeline-with-aporia-monitoring vs ml-pipeline-engineering