VevestaX
feast
Our great sponsors
| VevestaX | feast | |
|---|---|---|
| 10 | 8 | |
| 27 | 5,246 | |
| - | 1.7% | |
| 0.0 | 9.3 | |
| over 1 year ago | 6 days ago | |
| Jupyter Notebook | Python | |
| Apache License 2.0 | Apache License 2.0 |
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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đź“ťEverything 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
feast
- What's Happening with Feast?
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Running The Feast Feature Store With Dragonfly
Feast stands as an exceptional open-source feature store, revolutionizing the efficient management and uninterrupted serving of machine learning (ML) features for real-time applications. At its core, Feast offers a sophisticated interface for storing, discovering, and accessing features—the individual measurable properties or characteristics of data essential for ML modeling. Operating on a distributed architecture, Feast harmoniously integrates several pivotal components, including the Feast Registry, Stream Processor, Batch Materialization Engine, and Stores.
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Ask HN: How to Break into AI Engineering
AI Engineering is basically Data Engineering focused on AI. When in "traditional" Data Engineering you create pipelines that store processed data in something like a Data Lake, in AI Eng. your end storage might be a specialized Feature Storage (like Feast or GCP Vertex AI).
There are some AI Engineers with strong scientific/mathematical background, but that's rare. Usually, you're paired with these ML people that actually develop and evaluate the models.
So my advice is to start with Data Engineering and then find a specialization AI. You should have a VERY solid foundation on scripting and programming, specially Python. Also, a lot of concepts of "data wrangling". Understanding how data flows from point A to point B, how the intermediate storages and streaming engines work, etc. Functional programming is key here.
[0] https://github.com/feast-dev/feast
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In Need of Guidance: Implementing MLOps in a Complex Organization as a Junior Data Engineer
A feature store usually stores features which are used for training ML model. It is a centralized place for collaboration between data engineer, ML engineer, and data scientist, so that data engineer can write to the feature store while ML engineer and data scientist read from it. Hopsworks https://www.hopsworks.ai and feast https://github.com/feast-dev/feast are examples of open source feature store.
- [D] Your 🫵 Preferred Feature Stores?
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[P] Announcing Feast 0.10: The simplest way to serve features in production
Github: https://github.com/feast-dev/feast
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[D] What’s the simplest, most lightweight but complete and 100% open source MLOps toolkit? -> MY OWN CONCLUSIONS
Have you looked at Feats as a Feature Store solution? It seems promising but I haven't really looked into it yet though.
- Feast: OSS Feature Store for Production ML
What are some alternatives?
bodywork-pipeline-with-aporia-monitoring - Integrating Aporia ML model monitoring into a Bodywork serving pipeline.
kedro-great - The easiest way to integrate Kedro and Great Expectations
MLOps - End to End toy example of MLOps
featureform - The Virtual Feature Store. Turn your existing data infrastructure into a feature store.
vertex-ai-samples - Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud
Milvus - A cloud-native vector database, storage for next generation AI applications
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
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.
great_expectations - Always know what to expect from your data.
mlflow-easyauth - Deploy MLflow with HTTP basic authentication using Docker
mlrun - MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.