Putting machine learning into a database
Linear Digressions6 Huhti 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/

Tämä jakso on lisätty Podme-palveluun avoimen RSS-syötteen kautta eikä se ole Podmen omaa tuotantoa. Siksi jakso saattaa sisältää mainontaa.

Jaksot(308)

AI Agent Failure Modes (The Agents Season, Episode 6)

AI Agent Failure Modes (The Agents Season, Episode 6)

Despite what the marketing hype might suggest, AI agents are far from infallible — and if you've ever actually used one, you already know this. Today's episode dives deep into the many, varied, and so...

25 Touko 32min

Agentic Planning (The Agents Season, Episode 5)

Agentic Planning (The Agents Season, Episode 5)

When tackling a complex, multi-step task, even the smartest AI agent can fail without a solid game plan. This episode dives into the research around agentic planning — how agents move beyond simply re...

18 Touko 24min

Memory Management for AI Agents (The Agents Season, Episode 4)

Memory Management for AI Agents (The Agents Season, Episode 4)

Context windows are powerful — but finite, and surprisingly easy to overwhelm. When an AI agent is tackling a long, complex task, the information it needs has to fit inside that limited real estate, a...

10 Touko 24min

Lost in the Middle (The Agents Season, Episode 3)

Lost in the Middle (The Agents Season, Episode 3)

Just like a memorable talk lives or dies by its opening and closing, LLMs have a surprisingly similar quirk: they pay close attention to what's at the beginning and end of their context window — and k...

4 Touko 19min

ReAct and Tool Usage (The Agents Season, Episode 2)

ReAct and Tool Usage (The Agents Season, Episode 2)

Before 2022, there was a wall between AI and the real world — models could reason impressively, but couldn't look anything up, run code, or check whether anything they said was actually true. This epi...

27 Huhti 23min

What's an AI Agent? And Why's That Hard to Define? (The Agents Season, Episode 1)

What's an AI Agent? And Why's That Hard to Define? (The Agents Season, Episode 1)

AI agents are having a moment — and unpacking them properly takes more than a single conversation. This episode kicks off a dedicated multi-part season exploring AI agents from every angle, building u...

20 Huhti 19min

Unfaithful Chain of Thought

Unfaithful Chain of Thought

What's actually happening when an LLM "thinks out loud"? Research on human decision-making suggests that much of the reasoning we believe drives our choices is actually post hoc rationalization — we d...

13 Huhti 24min

Benchmark Bank Heist

Benchmark Bank Heist

What if an AI decided the smartest way to pass its test was to find the answer key? That's exactly what Anthropic's Claude Opus did when faced with a benchmark evaluation — reasoning that it was being...

6 Huhti 12min