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Show HN: PostgresML, now with analytics and project management

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Re: Show HN: PostgresML, now with analytics and project management

#41

Post by jorgemf »

montanalow wrote: post #20
jorgemf wrote: post #11

How do you deal with different dataset train/validation/test? How do you measure the degradation of the model? Is there any way to select the metric you target (accuracy, f1-score or any other)?

The data split technique is one of the optional parameters for the call to ‘train’. Model degradation is a really interesting topic, that is hopefully made less difficult when retraining is trivialized, but we also want to add deeper analytics into individual model predictions, as well as better model explanations with tools like shap. We haven’t exposed custom performance metrics in the API yet, but we’re computing…

When I was talking about metrics I meant metrics of the model (accuracy, precission, recall, mean square error, etc), not performance.

Re: Show HN: PostgresML, now with analytics and project management

#42

Post by jiocrag »

zmmmmm wrote: post #40

Seems like a great idea. When you look at many ML frameworks half the code and learning overhead is data schlepping code and table like structures that "reinvent" the schema that already exists inside a database. Not to mention, there can be security concerns from dumping large amounts of data out of the primary store (how are you going to GDPR delete that stuff later on?). So why not use it natively where the data a…

Yeah — seems like all you need is Snowflake-esque separation of storage and compute and bob’s your uncle.

Re: Show HN: PostgresML, now with analytics and project management

#44

Post by ekzhu »

Great idea! I see this is implemented using the Python language interface supported by PostgreSQL and importing sklearn models. I always wonder how scalable this is considering the serialization-deserialization overhead between Postgres' core and Python. Do you see any significant performance difference between this and training the sklearn models directly on something like Dataframes?

Re: Show HN: PostgresML, now with analytics and project management

#45

Post by montanalow »

obert wrote: post #43

is it possible, or how hard is it, to plug in custom proprietary models?

Custom proprietary models are on the TODO list. There are open questions about the mechanism to distribute the model params and code, which will lead to additional complexity, but I think it's something we'll eventually need to support.

In the opposite direction of bespoke fully custom models being the norm, I'd like to build more "rails" for ML. Hopefully we can expose enough hyperparams, even for deep learning architectures, and automatically adapt the inputs with configurable transformers so that we cover 90% of the custom use cases out of the box.

Re: Show HN: PostgresML, now with analytics and project management

#46

Post by montanalow »

ekzhu wrote: post #44

Great idea! I see this is implemented using the Python language interface supported by PostgreSQL and importing sklearn models. I always wonder how scalable this is considering the serialization-deserialization overhead between Postgres' core and Python. Do you see any significant performance difference between this and training the sklearn models directly on something like Dataframes?

This is an interesting benchmark I'll try to code up. Although, it seems a bit like an apples/oranges comparison, since a Dataframe in memory had to come from somewhere, either a CSV or database like Postgres, in which case I have my money on Postgres outcompeting the standalone process parsing CSV.

In the end though, it'll be important to have benchmarks for all the key steps in the process, both in terms of memory and compute. Off a hunch, I think the memory inefficiency involved in high level pandas operations is more likely to be a driving force to move operations into lower layers, than CPU runtime.

Re: Show HN: PostgresML, now with analytics and project management

#48

Post by coatue »

jiocrag wrote: post #42
zmmmmm wrote: post #40

Seems like a great idea. When you look at many ML frameworks half the code and learning overhead is data schlepping code and table like structures that "reinvent" the schema that already exists inside a database. Not to mention, there can be security concerns from dumping large amounts of data out of the primary store (how are you going to GDPR delete that stuff later on?). So why not use it natively where the data a…

Yeah — seems like all you need is Snowflake-esque separation of storage and compute and bob’s your uncle.

Looks like a good fit to run on Hydras.io (YC W22) the Postgres data warehouse that separates the PG query layer from compute / storage. Disclaimer, I'm the co-founder.

Re: Show HN: PostgresML, now with analytics and project management

#49

Post by pcj-github »

jpgvm wrote: post #4
jmuguy wrote: post #2

What affiliation does this have with PostgreSQL?

None, it's just an extension. Which is part of what is so awesome about PostgreSQL, everyone can build extensions that look and feel native and can do almost anything.

I found this to be very confusing and took some sorting out that this is not an official project of postgresql. I personally think a name change and logo redo is in order here.

Re: Show HN: PostgresML, now with analytics and project management

#50

Post by montanalow »

coatue wrote: post #48
jiocrag wrote: post #42

Earlier quoted context omitted.

Yeah — seems like all you need is Snowflake-esque separation of storage and compute and bob’s your uncle.

Looks like a good fit to run on Hydras.io (YC W22) the Postgres data warehouse that separates the PG query layer from compute / storage. Disclaimer, I'm the co-founder.

Nice! Feel free to file any issues on Github if something doesn't work. I'd love to understand more about the internals of the hydra architecture.
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