yanovskishai

I imagine it's hard to create a very generic tool for this usecase - what are the supported frameworks/libs, what does this tool assume about my implementation ?

Played with Bayesian nets a bit in grad school—Pearl’s causality stuff is still mind-blowing—but I’ve almost never bumped into a PGM in production. A couple things kept biting us: Inference pain. Exact is NP-hard, and the usual hacks (loopy BP, variational, MCMC) need a ton of hand-tuning before they run fast enough.

The data never fits the graph. Real-world tables are messy and full of hidden junk, so you either spend weeks arguing over structure or give up the nice causal story.

DL stole the mind-share. A transformer is a one-liner with a mature tooling stack; hard to argue with that when deadlines loom.

That said, they’re not completely dead - reportedly Microsoft’s TrueSkill (Xbox ranking), a bunch of Google ops/diagnosis pipelines, some healthcare diagnosis tools by IBM Watson built on Infer.NET.

Anyone here actually shipped a PGM that beat a neural baseline? Would really love to appreciate your war stories.

or at least look like sensitive info .. :-)

On a somewhat related note, due to the ongoing war in Israel, GPS is being heavily jammed to deter terrorist drone attacks. An intriguing side effect of this is that services like "Find My Phone" and AirTag are experiencing disruptions. This interference sometimes results in devices appearing to be located in neighboring countries, hundreds of miles away from their actual location. Here's an article detailing the jamming situation: https://www.gpsworld.com/gps-jamming-in-israel/.

Huge resources reference on ML and data engineering topics open for collaborative contributions.

Are you aware of any similar resources on DS or general software engineering topics?

Reminds an idea from Tarkovsky's Stalker

https://en.m.wikipedia.org/wiki/Stalker_(1979_film)

Despite the fact that the film is officially based on Strugatsky 1972 novel Roadside Picnic, the sphiere idea from resemblance 'the Room'.

Hi Congrats for the launch! Will be happy to learn more about how it compares to other key-value stores (e.g DynamoDB, Redis)

Anyone got a chance to try the recently launched Hash.ai? From their announcements it seemed like a tool aiming to more complex scenarios.

As someone who spent handreds of hours writing (and cursing) SQL queries -it definitely sounds compelling.

Thanks ! There are indeed players many new in the data versioning space (DVC and Quilt also probably worth mentioning).

I totally agree that data management problems are not just ML related. But I personally think that there are additional challenges in the space that are not just version control for data.. all the area of data quality management and monitoring for example. I liked the analogy to devops, source version was super critical problem to solve in software development, but it didn't stop there, with things like CI/CD etc. I believe we'll see similar evolution in the data space..

thanks!

thanks!

Could you please mention what are the other solutions you've got to see in this space?

How airplane wing really works.

He mention in the README that sklearn native pipelinea don't support estimators union (stacking).

It was exactly what I thought. Especially they explicitly say

>"With Manifold’s design, we turned the traditional ML model visualization challenge on its head. Instead of inspecting models, we inspect individual data points "

It really seems that they went on implanting the more sophisticated step (which seems very promising), while skipping the straight forward one.

What am I missing?

Very interesting approach.

I advice checking out this tutorial http://www.whatimade.today/esp8266-easiest-way-to-program-so... I found it very helpful

Seems promising! I wonder how long would it take to be available in other countries as well ..

Israel