Data Science Articles

Ethics in the way of scale

For NYT Opinion, Paul Ford on the challenges for AI companies to build… Tags: ethics, New York Times, Paul Ford

Flowing Data

Best way to translate machine learning model in Python to SQL script?

After building an ensemble machine learning model in Python I'd like to translate the model into SQL script so we can score new data in MS SQL Server Management Studio. After some googling the m2cgen module looked promising, unfortunately it does not support Python to sql translation (despite the Google AI summary saying otherwise). Are there any other options? I see it's possible to run Python code within MS SQL Server Management Studio. It requires installing SQL Server Machine Learning Serv

Reddit r/datascience

My Workflow for Understanding LLM Architectures (Sebastian Raschka)

submitted by /u/rhiever [link] [comments]

Reddit r/datascience

A Career in Data Is Not Always a Straight Line, and That’s Okay

Sabrine Bendimerad on why flexibility is a crucial data science skill, the risks of outsourcing human thinking to AI agents, and the changing terrain of career paths today. The post A Career in Data Is Not Always a Straight Line, and That’s Okay appeared first on Towards Data Science.

Towards Data Science

How are you helping your company understanding the limitations of AI derived data?

From my perspective, one of the biggest challenges of data science as a field right now is the tension between: A) AI can give "pretty good" answers extremely fast and democratizes it B) Those answers are often decent, but could be nontrivially "wrong" C) That "wrongness" is often not exposed for months or years That is, AI fully democratizes "getting a number" to our biz stakeholders across just about any business problem. A lot of times that number is off some but still pretty good and us

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How Spreadsheets Quietly Cost Supply Chains Millions

A simulation of how a single forecast change moves through five planning teams, and why most retailers lose money in the gap between Sales and Stores. The post How Spreadsheets Quietly Cost Supply Chains Millions appeared first on Towards Data Science.

Towards Data Science

Comparing Explicit Measures to Calculation Groups in Tabular Models

With the advent of UDFs and their combination with calculation groups, I see a lot of discussion about not creating explicit measures but instead offering calculation groups to report creators. The post Comparing Explicit Measures to Calculation Groups in Tabular Models appeared first on Towards Data Science.

Towards Data Science

10 Python Libraries for Building LLM Applications

Learn the top Python frameworks for LLM apps covering fine-tuning, model loading, serving, RAG pipelines, multi-agent systems, and evaluation.

KDnuggets

Build a British voter

The Economist shows probabilities that a person votes for each party, given a… Tags: Economist, election, voting

Flowing Data

Bytes Speak All Languages: Cross-Script Name Retrieval via Contrastive Learning

Why learn 8 scripts when you can learn 256 bytes? The post Bytes Speak All Languages: Cross-Script Name Retrieval via Contrastive Learning appeared first on Towards Data Science.

Towards Data Science

Standardization vs Log transform ?

I have been trying to understand the use cases of both of these and I am really confused. I know log transform fixes the features and makes their distribution normal and standardization on the other hand only fixes the scale of the feature by keeping the distribution the same. Are these things which I use one after the other ? Or just simply use one depending on the case (which I also don't understand when) ? submitted by /u/-Cicada7- [link] [comments]

Reddit r/datascience

Causal Inference Is Different in Business

How does decision-gravity dictate this gap? The post Causal Inference Is Different in Business appeared first on Towards Data Science.

Towards Data Science