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 .. :-)

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?

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!