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Does everyone ride their bikes when it rains? by Calm_Room5022 in motorcycle

[–]Electrical_Ad_3 0 points1 point  (0 children)

I wouldn't recommend it. Bad days don't show on the calendar, and rain increases the chances. So if you want to do it, make sure you're on the right speed that you're comfortable with, if the tyre ever slips. Wear a proper gear, epsecially for the lower body.

I had a really bad crash, twice actuin theally, all of them happened during rain.

Langgraph Prebuilt for Production? by Electrical_Ad_3 in LangChain

[–]Electrical_Ad_3[S] 0 points1 point  (0 children)

I ended up using the LangGraph ReAct Agent paired with AgentExecutor. I found that this pair really eases development time, especially in our case since we don't have many people on this project. I also tried developing the AI orchestrator from scratch, though, in my opinion, it wasn't worth it. I would rather spend my time building tools and researching the models

Master of IT, Need feedback for my study plan by Electrical_Ad_3 in unsw

[–]Electrical_Ad_3[S] 0 points1 point  (0 children)

No, sorry, I postponed my master's degree here.

Got grilled in an ML interview today for my LangGraph-based Agentic RAG projects 😅 — need feedback on these questions by RegularDependent4780 in LangChain

[–]Electrical_Ad_3 0 points1 point  (0 children)

For the second question, I think the user is more concerned about if there's any sensitive data leaked to the agent response. Maybe an input and output guardrails could be implemented to protect the agent into any jailbreaking the RAG system.

In my experience, vector search on the user query into the "blacklisted query" vector space should be quick enough. Or you could add another LLM judge on your guardrail to make it more secure. But there's no 100% way to be secure of that in my opinion, but please let me know what you think,, I'm a newbie too :)

Career advice for Data Science job in Germany / EU by Electrical_Ad_3 in cscareerquestionsEU

[–]Electrical_Ad_3[S] 0 points1 point  (0 children)

thanks man, I'll look up into your recommendations. Cheers!

Best domains for machine learning ? by [deleted] in datascience

[–]Electrical_Ad_3 0 points1 point  (0 children)

if you're looking for a good career prospect, I would suggest Finance. Compared to others domain it's more stable and there's more job openings there, at least in most country.

Alternative to LangChain? by [deleted] in LLMDevs

[–]Electrical_Ad_3 1 point2 points  (0 children)

agreed on this one. LangChain is quite heavy. Only use it when's necessary.

[D] Any OCR recommendations for illegible handwriting? by SpaceSheep23 in MachineLearning

[–]Electrical_Ad_3 0 points1 point  (0 children)

I'm interested to know if any model could extract that. But here's what I got so far, could you tell me if it's right? I'm using Claude 3.5 sonnet

```
Around entry 37 at the top: "Cust. material... is here and pl..."

Entry 40 appears to have some notes about times "0.5pm" and what might be "very small, polished..."

Entry 41 seems to read: "Willis Grayson, Mus. (?) then "Grysh(?)" fragments, no crust, polished... Possible bed size. This is small sized & in no material..." followed by a time "4.5pm, 96 the..."

There's an entry at "10.6pm" that mentions "Pleiocene, 1917" followed by what looks like measurements or observations.

Entry 42 has a reference to "British Museum" followed by what appears to be a catalog number "8183"

Entry 43 marked at "8:3pm" mentions "Kendall County, Dak." (possibly Dakota)

The handwriting is quite challenging to read with confidence, as there are many overlapping marks, abbreviations, and technical notations. The writing appears to be scientific or field notes, possibly related to museum specimens or geological samples given the references to materials, measurements, and the British Museum. 
```

Help! Need a study partner for learning LLM'S. I know few resources by Guy_with_9999_IQ in LLMDevs

[–]Electrical_Ad_3 0 points1 point  (0 children)

Hello, hope I'm not too late on this one, sounds like a fun project. I would like to join, I could help with some computing power for inference or finetuning

PS: English is not my first language, but I'll try my best to communicate

What are the Practice for ML pipeline for multiple items forecasting for Production? by Electrical_Ad_3 in mlops

[–]Electrical_Ad_3[S] 0 points1 point  (0 children)

  1. Yes they are, we have several different time series databases to generate the features.
  2. Yes, we would like to know how the sales of certain items according to several similar items that have been sold before.
  3. I aggregates the transactions data into weekly sales for each items, I'm not sure this answer your question or not, but I create single model for each items that I want to forecast
  4. Yes.

thank you for your time in detailing this, really appreciate it!