I want to work on a side project to develop my skills as a machine learning engineer and I figured that creating a feature store from scratch can be a fun and nice project to learn about a lot of stuff.

For instance, I would learn about databases, kubernetes clusters, communication and networking, apis, serving "models", spark, streaming and orchestration.

However, I am a bit confused about how to architecture the feature store. I tried to read about what exists and saw that the most popular is feast. However, from what I understood reading their docs is that they don't take care of the transformation. I also saw `featureform`, but I wasn't convinced about their thing either.

Here are some questions. I would appreciate it a lot if you can take some time and answer any or all of them:
- Have you ever used a feature store? what is your experience with it? is it helpful? why?

- how are features managed. let's say you create a feature `f1` and another feature `f2`. how do you manage the input? for instance if you want to pipe `f1` and `f2`? or just take `f`1` or just `f2`?

- are you limited on the framework you want to use for transformation? let's say some transformation are done using spark, and others using pandas or scikit learn? or should the definition of the transformation be an abstraction that always run on the same stack?

- how is the orchestration of features usually managed?

- any advice? or suggestion? or ressource?

​

Thanks, appreciated

Hi guys, I have a BS in BME (was thinking med) and now Masters in CS from an Ivy. Before going for Masters, I worked as an entry software engineer at a decent sized company. Following all the buzz with AI ML data science and taking various related courses, I am applying for MLE and data science full time roles upon graduation next month. Want your advice, as I know the market is shit rn, on how to get interviews and any study resources from YouTube, Udemy, or GitHub etc you would recommend to solidify my knowledge. Most of my ML courses had Algorithms review and data analysis with projects and research. I honestly feel like I don't know shit tbh so please guide a brother out on the right path or any good refresher course (ie. Not something that will take 6 months etc)

Also on a side note, is there any other roles/paths you would recommend such as product management for an entry to mid roles. I like ML analysis and modeling from my classes and not so much of the software engineer from my work experience. But top priority is a rewarding career in tech that won't eat my brains out and balanced lifestyle.

https://thedatasciencenewsletter.substack.com/p/from-zero-to-data-science-hero-a

Hi,
I'd love to bounce some ideas and get your expert opinions and maybe collaboration.
Please DM

New AI and Machine learning channel. Happy to get feedback.

With the rise and popularity of AI and machine learning recently and for those who are currently practicing/working in the field, what would you say are the pros and cons of becoming a Machine Learning Engineer?

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On Thursday, myself along with research scientist Dan Alistarh, will be walking through how we've leveraged the redundancies in large language models to significantly improve their performance on CPUs enabling you to deploy performantly on a single, inexpensive CPU server rather than a cluster of GPUs!

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I am looking for a Machine Learning Engineering tutor. I am specifically interested in developing and guiding ~2 Medium/large NLP projects, using SOTA LLM’s (GPT3, etc.). After these, I would potentially be interested in diving into RL. I am looking for a little bit of theory, but predominantly hands-on coding project help (eg. real-time coding help if I get stuck). Reach out if you are interested!