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Building RAG-based LLM Applications for Production (Part 1)

In this guide, we will learn how to develop and productionize a retrieval augmented generation (RAG) based LLM application, with a focus on scale, evaluation and routing.

https://www.anyscale.com/blog/a-comprehensive-guide-for-building-rag-based-llm-applications-part-1
Logparser

Logparser provides a machine learning toolkit and benchmarks for automated log parsing, which is a crucial step for structured log analytics

https://github.com/logpai/logparser
QuasiQueue

QuasiQueue is a MultiProcessing library for Python that makes it super easy to have long running MultiProcess jobs. QuasiQueue handles process creation and cleanup, signal management, cross process communication, and all the other garbage that makes people hate dealing with multiprocessing.

https://github.com/tedivm/quasiqueue
Compiling ML models to C for fun

ML models can be compiled to graphs, which can be traversed to perform forward and backward passes. This approach can improve performance and make it easier to debug ML models.

https://bernsteinbear.com/blog/compiling-ml-models/
Building A RisingWave Connector for Django ORM

Ever wanted to connect a streaming database to Django ORM. Learn how with Django and RisingWave.

https://bas.codes/posts/django-risingwave