bodywork-pymc3-project
Serving Uncertainty with Bayesian inference, using PyMC3 with Bodywork (by bodywork-ml)
bodywork
ML pipeline orchestration and model deployments on Kubernetes. (by bodywork-ml)
| bodywork-pymc3-project | bodywork | |
|---|---|---|
| 1 | 8 | |
| 13 | 430 | |
| - | - | |
| 5.3 | 0.0 | |
| almost 2 years ago | 9 months ago | |
| Jupyter Notebook | Python | |
| MIT License | GNU Affero General Public License v3.0 |
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.
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
Posts with mentions or reviews of bodywork.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-05-17.
- Deployment automation for ML projects of all shapes and sizes
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A tutorial on how to handle prediction uncertainty in production systems, by using Bayesian inference and probabilistic programs
how to deploy it to Kuberentes using Bodywork.
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[P] [D] How are you approaching prediction uncertainty in ML systems?
I usually turn to generative models - e.g. probabilistic programs and Bayesian inference. I’ve written-up my thoughts on how to engineer these into a ‘production system’ deployed to Kubernetes, using PyMC and Bodywork (an open-source ML deployment tool that I contribute to).
- Bodywork: MLOps tool for deploying ML projects to Kubernetes
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Tool for mapping executable Python modules to Kubernetes deployments
I’m one of the core contributors to Bodywork, an open-source tool for deploying machine learning projects developed in Python, to Kubernetes.
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[P] [D] The benefits of training the simplest model you can think of and deploying it to production, as soon as you can.
I’ve had many successes with this approach. With this in mind, I’ve put together an example of how to make this Agile approach to developing machine learning systems a reality, by demonstrating that it takes under 15 minutes to deploy a Scikit-Learn model, using FastAPI with Bodywork (an open-source MLOps tool that I have built).
- bodywork - MLOps for Python and K8S
- bodywork-ml/bodywork-core - MLOps automation for Python and Kubernetes