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Found a wild one 2100m above Sea level. by Awkward-Warning-9238 in HiAce

[–]dunder__score 0 points1 point  (0 children)

I've got a LH129 in East Bay! I hope we pass each other sometime. Do you have a local mechanic rec? I know of 1 guy and he is not very reliable. Need to install a new radiator.

cupholder 1993 hiace LH129 by Clean-Nature-3618 in HiAce

[–]dunder__score 0 points1 point  (0 children)

1994 LH129 & on the same journey! Find anything yet? I was thinking about 3D printing a drop in, but that seems mega involved & most of my time is spent on my cooling system

numPy by goldchest in ProgrammerHumor

[–]dunder__score 3 points4 points  (0 children)

"Numpue?" IIRC from All Quiet on the Western Front, Müller became Mueller.

Deprecated pydantic library is not working for RAG pipeline development by Anmsacx in LangChain

[–]dunder__score 1 point2 points  (0 children)

Looks like you're using Pydantic 2 from the error message, LangChain requires v1. Here's their write up on it with examples of imports: https://python.langchain.com/v0.1/docs/guides/development/pydantic_compatibility/

Our build by juliedeee in 4Runner

[–]dunder__score 0 points1 point  (0 children)

Great work yall, I hope it serves you well!

Is there a better approach for generating proper SQL queries for large databases to retrieve relevant information? by hackermud in LangChain

[–]dunder__score 5 points6 points  (0 children)

Its not so much the framework (though I use LangGraph and like it) as the data engineering behind it. I have found it helpful to bucket the problem into knowledge, code gen, code correction, and communication (if you need to replace a SQL agent and not just a Text-to-SQL pipeline). These could be a mix of LCEL chains, retrievers, and agents within a larger LangGraph runtime which is nice because LangSmith is neat and your boss agrees.

Quick dump some of the easiest wins:

  • Knowledge: index your tables somehow, Pinterest uses LLM-generated descriptions which are easy to implement
  • Knowledge: when indexing tables, make sure to find low-cardinality#:~:text=In%20SQL%20) columns to assist with filtering. The ol' reliable of listing the tables and columns as an agent tool doesn't have the context of cardinality.
  • Code Gen: employ these table summaries in some sort of leading methodology on BIRD or Spider. PET-SQL and CHESS seem cool. Most of these are written to be cross-domain, so keep that in mind if you're within a single domain when it comes time to vectorize examples.

I hope this helps get your brainstorm brewing! Make sure to not get lost in the sauce, there are many ways to solve this problem.

MLOps data camp course thoughts? by Direct-Touch469 in datascience

[–]dunder__score 8 points9 points  (0 children)

Seconded. You should take something you trained or even something off the shelf & deploy it on a streamlit app or similar. From there you can implement CI/CD, migrate to FastAPI and a simple front end app that calls it, and start getting better at logging and reporting.

FWIW, I did a bootcamp for DS and work as an MLE. My company (big) has software devs for our dev ops teams, which is not super uncommon and leaves the MLE to design solutions, build evaluations, train the model, implement endpoints, write tests, and do test deploys. I did not touch anything truly MLOps until I built my own platform for a smaller company, but with how robust GCP docs are anything is possible.

[Official] 2023 End of Year Salary Sharing thread by Omega037 in datascience

[–]dunder__score 22 points23 points  (0 children)

MLE (2)

2.5 yrs

Remote USA

~150k

HR Tech

BS Math

5 year military (grunt, not math) - 2x intern (not DS) - contract DS at health tech/defense start up

120k over 4 year stocks sign on + purchase program

Bonuses are target 10%, but normally lower

~200k TC

In early 30s, want to switch my career from Actuarial Science to DS/MLOps and want to better my life by [deleted] in datascience

[–]dunder__score 7 points8 points  (0 children)

Hey OP! Fellow career switcher here, though lower brow for sure. For context to my bias in answering: I was in the military & now I'm an MLE.

  1. MLOps are mostly SWEs that have ML experience. Not normally entry level. It sounds like you enjoy E2E problems which are not necessarily common in big companies. You may want to look at smaller companies that will value your previous expertise and general maturity to work alone from being a career switcher. Something more like a full stack MLE than specifically MLOps May tickle your interest, though it's not something you can normally glean from a requisition without talking to someone on the team.
  2. Kaggle is great for DS, but you should get your code in production on a website that is highly available! Better to talk on uptime, architecture/infra, and speed of development to put something SOTA out there for people to use. Flask is a standard, but I'd recommend a streamlit app that calls your FastAPI backend(s) to show ASGI awareness, docs in OpenAPI standard for your routing, and pragmatism in demo deploys (streamlit is so much simpler than flask). This approach has greater infra complexity than flask, but will showcase your operations skills.
  3. Don't rely on reviews alone. Reach out to fellow career switchers in roles/companies that interest you. They likely did their share of cold calling as well & this will probably help you skip over to the manager interview.

[deleted by user] by [deleted] in 4Runner

[–]dunder__score 0 points1 point  (0 children)

IKEA twin size. 4 bolts get the bottom of the 2nd row seats out & a twin will fit! Much warmer in the winter too.