Things that I'm saving to read or start to learn someday and other things that I send to people when they ask me about how to start with.
Maybe if I sort them I'll be more motivated to involved with. Most of the contents are twitter threads and in spanish because I mostly get them from my twitter feed _
##Landscape God The LF AI & Data landscape explores open source projects in Artificial Intelligence and Data and their respective sub-domains. LF AI & Data Foundation Interactive Landscape
Machine Learning Operations (MLOps): Overview, Definition, and Architecture" - A paper that covers the principles, components, roles, architecture and workflows for MLOps by Dominik Kreuzberger, Niklas Kühl, Sebastian Hirschl. Paper
Info from @TheSequenceAI: 5 open-source frameworks for MLOps:
- MLflow: end-to-end ML lifecycle management
- TorchServe: a super simple serving framework
- ONNX: ML interoperability
- Lyft’s Amundsen: data discovery & versioning
- CML: CI/CD in ML pipelines
Super cool recopilation of machine learning course notes on all topics related to machine learning, NLP, and AI. Repo
Whitepaper Kubernetes benchmarking study 2022 Whitepaper
A super complete thread with free repos Link
Pseudocode to start learning the basics Link
Open Bootcamp. Totally free, adaptative learn from begginer to full-stack, they help you to find work Page Seen in @baumannzone
Codefinity to make your learning path and maybe start with them? Link Other advices in a short thread where I get the pseudocode idea link
Advices about starting working in english for the first time link
Better understanding of branches Resource
Twitter thread explaining how to do a good CV Link The Ultimate Guide to Machine Learning Job Link
Code challenges with code and videos explaination by MoureDev Github