Linear Digressions6 Apr 2020

Putting machine learning into a database

Most data scientists bounce back and forth regularly between doing analysis in databases using SQL and building and deploying machine learning pipelines in R or python. But if we think ahead a few years, a few visionary researchers are starting to see a world in which the ML pipelines can actually be deployed inside the database. Why? One strong advantage for databases is they have built-in features for data governance, including things like permissioning access and tracking the provenance of data. Adding machine learning as another thing you can do in a database means that, potentially, these enterprise-grade features will be available for ML models too, which will make them much more widely accepted across enterprises with tight IT policies. The papers this week articulate the gap between enterprise needs and current ML infrastructure, how ML in a database could be a way to knit the two closer together, and a proof-of-concept that ML in a database can actually work. Relevant links: https://blog.acolyer.org/2020/02/19/ten-year-egml-predictions/ https://blog.acolyer.org/2020/02/21/extending-relational-query-processing/

Det här avsnittet är hämtat från ett öppet RSS-flöde och publiceras inte av Podme. Det kan innehålla reklam.

Avsnitt(331)

KV Cache Fundamentals

The unsung hero of every LLM conversation you've ever had? A clever optimization called the KV cache. Self-attention is computationally brutal — every token attending to every other token means costs ...

2 Nov 24min

AI Inference 101

Everyone talks about training the model — the data, the compute, the alignment magic. But what happens after the weights are frozen and millions of users start hammering it with prompts? That's infere...

26 Okt 27min

Back Next Week

Back Next Week by Katie Malone

19 Okt 18s

Back in Two Weeks

Back in Two Weeks by Katie Malone

12 Okt 21s

Mechanistic Interpretability: How Researchers Try To See Inside Models

As AI systems grow more powerful, they grow harder to understand — and that tradeoff has never mattered more. Most interpretability tricks give us approximations from the outside, but mechanistic inte...

5 Okt 29min

LLM As A Judge

Labeled data is expensive, slow, and painfully hard to come by — and when you're trying to evaluate whether an AI workflow is actually doing what you want, the problem gets even messier. How do you ju...

28 Sep 30min

The Impact of AI on Podcasting (Harvard Data Science Review Cross-Post)

Originally aired on the Harvard Data Science Review podcast. What can podcasting teach us about AI — and what can AI teach us about the future of podcasting? Katie Malone (that's our host) joins Jon ...

21 Sep 29min

Better Know A Benchmark: ExploitGym

When OpenAI's frontier models were caught hacking Hugging Face's servers, most people assumed they were hunting for answer keys. The real story is stranger and more unsettling. Katie and Phoebe unpack...

14 Sep 32min