bodywork-pymc3-project
whylogs-examples
| bodywork-pymc3-project | whylogs-examples | |
|---|---|---|
| 1 | 1 | |
| 13 | 48 | |
| - | - | |
| 5.3 | 1.8 | |
| almost 2 years ago | over 1 year ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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bodywork-pymc3-project
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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.
whylogs-examples
What are some alternatives?
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bodywork - ML pipeline orchestration and model deployments on Kubernetes.
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indaba-pracs-2022 - Notebooks for the Practicals at the Deep Learning Indaba 2022.
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.