applied-ml VS pipebase

Also, have any of you stumbled across any cool GitHub repos like this one: https://github.com/eugeneyan/applied-ml ?

The second repo I LOVE is Eugene Yan’s Applied ML repository. This is a brilliant idea to create and actually something I was planning on sort of casually doing in my non-existent free time… Anyhow, it is a curated list of technical posts from top engineering teams (Netflix, Amazon, Pinterest, Linkedin, etc.) detailing how they built out different types of AI/ML systems (e.g. forecasting, recommenders, search and ranking, etc.). Ofc, it focuses on AI/ML, but something similar could be made for the traditional or BI-oriented analytics stack, as well as the streaming world, super high value for practitioners! Btw-one of my favorite things at BCG used to be looking at our IT architecture team’s reference architecture diagrams… the best way to understand technologies is to look at how a ton of stuff is architected… and its fun!

Applied ML: A collection of papers, articles, and blogs on ML in production by different companies (Netflix, Uber, Facebook, LinkedIn, etc)

Eugene Yan's applied-ml has tons of case studies.

https://github.com/eugeneyan/applied-ml You may find some of his links interesting. I would avoid anything that refers to scaling up a platform as these are more backend engr focus. The more relevant posts to you are probably on the scale of blog posts that are product oriented like the ones I listed in section 4 (e.g. we wanted to solve X for our users and this is how we scoped and defined it). The technical aspects should come backseat to the business aspects. There's def a lot of companies/blog posts that he missed, but the internet is huge.

Check the resources mentioned in applied-ml. It includes blog posts/papers from many companies describing how they built some ML product X.

Check out this repo. They collect hundreds of case studies, broken down by dozens of methodologies from large real-world companies such as AirBnB, Nvidia, Uber, Netflix etc.

In general, the framework allow developer customize data pipeline through manifest definition and wire a variety of system through pipeware plugins to sync/transform data.

pipebase is a low code data integration framework.

If you want to build app locally, here is a tutorial for a timer (your first 'Hello World' app).

If you want to learn more about how to define/compose your pipeline manifest - the YAML file, see [`doc`](https://github.com/pipebase/pipebase/blob/main/pipegen/README.md)

pipebuilder released ! pipebuilder is a CI for pipebase application. pipebase is a framework to allow developer compose/build low code data integration app (ex: ETL) in YAML manifest. Here is a list of supported connection pipeware. pipebuilder enable developer submit build task, download app binary through command-line tool pbctl against CI server.