featureform
feast
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featureform | feast | |
---|---|---|
28 | 8 | |
1,667 | 5,216 | |
1.4% | 2.7% | |
9.7 | 9.3 | |
2 days ago | 1 day ago | |
Jupyter Notebook | Python | |
Mozilla Public 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.
featureform
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What’s your process for deploying a data pipeline from a notebook, running it, and managing it in production?
Feature store: new hot one: https://www.featureform.com/
- [D] Your 🫵 Preferred Feature Stores?
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How to Build a Recommender System with Embeddinghub
Usually embeddings — dense numerical representations of real-world objects and relationships, expressed as a vector — are stored in database servers such as PostgreSQLEmbedding. However Embeddinghub makes it easier to store your embeddings and load them. You can get started with minimal setup, and it also makes your code look less verbose as compared to, say, building a KNN model using scikit-learn.
- [P] Embeddinghub: A vector database built for ML embeddings
feast
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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.
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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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[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.
What are some alternatives?
kedro-great - The easiest way to integrate Kedro and Great Expectations
Milvus - A cloud-native vector database, storage for next generation AI applications
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
great_expectations - Always know what to expect from your data.
mlrun - Machine Learning automation and tracking
feathr - Feathr – A scalable, unified data and AI engineering platform for enterprise
clearml - ClearML - Auto-Magical CI/CD to streamline your ML workflow. Experiment Manager, MLOps and Data-Management
streamlit - Streamlit — A faster way to build and share data apps.
BentoML - Build Production-Grade AI Applications
deepchecks - Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.