VevestaX
bodywork-pymc3-project
Our great sponsors
| 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 |
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
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📝Everything you need to know about Distributed training and its often untold nuances
100 early birds who login into www.vevesta.com will get a free lifetime subscription.
- [D] Open Source library to do automatic EDA + experiment tracking in a spreadsheet
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[D] ZIP models as a means to handle regression on data with excess of zeros
Sharing an article on how to handle regression for data which has lots and lots of zeros. VevestaX/ZIP_tutorial.md at main · Vevesta/VevestaX · GitHub
- Zero Inflated Poisson Regression Model – How to model data with lot of zeroes?
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MLflow VS VevestaX - a user suggested alternative
2 projects | 12 May 2022
- Show HN: Discover VevestaX – Track ML features, experiments and EDA in an Excel
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[D] Impactful Computer Vision Research - Nerf (Neural Radiance Fields)
On side note, we have developed a knowledge repository for Machine Learning Projects with note taking ability. We are looking for beta testers. Check us out on www.vevesta.com or mail us on [[email protected]](mailto:[email protected]). Eager to hear your views on the same.
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VevestaX - An awesome and simple tool to track ML experiments in an excel file
You can check out the source code at our GitHub page: https://github.com/Vevesta/VevestaX.
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VevestaX - Library to track ML experiments and data into an excel file
Gitlink: https://github.com/Vevesta/VevestaX
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.
What are some alternatives?
bodywork-pipeline-with-aporia-monitoring - Integrating Aporia ML model monitoring into a Bodywork serving pipeline.
MLOps - End to End toy example of MLOps
amazon-sagemaker-examples - Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
vertex-ai-samples - Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud
bodywork - ML pipeline orchestration and model deployments on Kubernetes.
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
indaba-pracs-2022 - Notebooks for the Practicals at the Deep Learning Indaba 2022.
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.
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.
mlflow-easyauth - Deploy MLflow with HTTP basic authentication using Docker
whylogs-examples - A collection of WhyLogs examples in various languages