GitHub - arghhjayy/EndToEndML: End to end ML pipeline written with open source tools exclusively
github.com
GitHub - arghhjayy/EndToEndML: End to end ML pipeline written with open source tools exclusively
sari added
Deep-ML
deep-ml.comEach of these broad stages of the Machine Learning workflow (Data Preparation, Model Building and Production) have a number of vertical functions. Some of these functions are part of a larger end-to-end platform, while some functions are the main focus of some platforms.
Aparna Dhinakaran • ML Infrastructure Tools for Model Building
sari added
GitHub - FlowiseAI/Flowise: Drag & drop UI to build your customized LLM flow
github.comAndrés added
VectorDB-recipes
Dive into building GenAI applications! This repository contains examples, applications, starter code, & tutorials to help you kickstart your GenAI projects.
Dive into building GenAI applications! This repository contains examples, applications, starter code, & tutorials to help you kickstart your GenAI projects.
- These are built using LanceDB, a free, open-source, serverless vectorDB that requires no setup .
- It integrates into python data ecosystem so you can simply start using these
lancedb • GitHub - lancedb/vectordb-recipes: High quality resources & applications for LLMs, multi-modal models and VectorDBs
Nicolay Gerold added
From a software maintenance perspective there is little consensus on how to organize ML projects. It feels like websites before Rails came out: a bunch of random PHP scripts with an unholy mixture of business logic and markup sprinkled throughout.
tinyclouds.org • Google Brain Residency
Jimmy Cerone added
.pocket Wow. Still the same today. Big opportunity. Though folks like Hugging Face are starting to change this.
We found the ML engineering workflow to revolve around the following stages (Figure 1): (1) Data Preparation , which includes scheduled data acquisition, cleaning, labeling, and trans-formation, (2) Experimentation , which includes both data-driven and model-driven changes to increase overall ML performance, and is typically measured by metrics suc... See more
Shreya Shankar • "We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learning.
Nicolay Gerold added