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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

#61

Post by mr_toad »

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…

It looks like this is running Python to do the actual ML?

There’s nothing stopping you from reading database table structures directly into memory in Python or R now. You don’t need an intermediate data store.

I agree that running training on production instances would be a bad idea. First, you need to denormalise data for ML, and secondly you typically don’t want your training data to be constantly changing.

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

#62

Post by montanalow »

mr_toad wrote: post #61
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…

It looks like this is running Python to do the actual ML? There’s nothing stopping you from reading database table structures directly into memory in Python or R now. You don’t need an intermediate data store. I agree that running training on production instances would be a bad idea. First, you need to denormalise data for ML, and secondly you typically don’t want your training data to be constantly changing.

Outside of academia, you need to be able to constantly adapt to changing training data. That is a constant pitfall for many projects that try to transition from offline to online.

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

#65

Post by mr_toad »

montanalow wrote: post #62
mr_toad wrote: post #61

Earlier quoted context omitted.

It looks like this is running Python to do the actual ML? There’s nothing stopping you from reading database table structures directly into memory in Python or R now. You don’t need an intermediate data store. I agree that running training on production instances would be a bad idea. First, you need to denormalise data for ML, and secondly you typically don’t want your training data to be constantly changing.

Outside of academia, you need to be able to constantly adapt to changing training data. That is a constant pitfall for many projects that try to transition from offline to online.

Outside academia you need to do a lot of data cleaning and feature engineering, and if you’re constantly changing the model as well as the data you’ll never be able to attribute changes to either.

Chances are a DBA wouldn’t consider letting you do data engineering in a live production database anyway, so this really is all academic.

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

#66

Post by chaps »

mr_toad wrote: post #65
montanalow wrote: post #62

Earlier quoted context omitted.

Outside of academia, you need to be able to constantly adapt to changing training data. That is a constant pitfall for many projects that try to transition from offline to online.

Outside academia you need to do a lot of data cleaning and feature engineering, and if you’re constantly changing the model as well as the data you’ll never be able to attribute changes to either. Chances are a DBA wouldn’t consider letting you do data engineering in a live production database anyway, so this really is all academic.

Haven't played with it yet, but this tooling is exactly what I'm looking for for my SQL-first-python-second "data lake" workflows. It doesn't exactly matter to me if data in a table gets rewritten in-place since it's built on a model meant to be destroyed at any time from an original source file - generally data from FOIA or gov sources that's write-once.

In a way, having to pull things out of the DB and into python is something that requires change attribution on its own since it's a conversion between abstractions which I tend to think of as a lossy process (even if it isn't). This sort of tooling keeps the abstractions localized, so it's much easier to maintain a mental model of models of changes.

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

#67

Post by ekzhu »

montanalow wrote: post #46
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…

The Dataframe is loaded from disk true, but it is possible that batch loading is faster (esp. with structured data) than row-by-row translation Postgres types into Python types. Would be interesting to see the benchmark results.

> 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.

Indeed. Not only memory but also inefficiency related to Python itself. It would be great if feature engineering pipelines can be pushed down to lower layers as well. But for now, the usability of Python is still unparallel.

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

#68

Post by code_biologist »

mr_toad wrote: post #65
montanalow wrote: post #62

Earlier quoted context omitted.

Outside of academia, you need to be able to constantly adapt to changing training data. That is a constant pitfall for many projects that try to transition from offline to online.

Outside academia you need to do a lot of data cleaning and feature engineering, and if you’re constantly changing the model as well as the data you’ll never be able to attribute changes to either. Chances are a DBA wouldn’t consider letting you do data engineering in a live production database anyway, so this really is all academic.

Outside academia you need to do a lot of data cleaning and feature engineering, and if you’re constantly changing the model as well as the data you’ll never be able to attribute changes to either.

I get the concern but sometimes I really just do want a black box regressor or classifier. Model performance monitoring is important, but I don't care about attribution.

Chances are a DBA wouldn’t consider letting you do data engineering in a live production database anyway, so this really is all academic.

Maybe it isn't data engineering, but I'm curious what you'd call using Google's BigQuery ML? "BigQuery ML enables users to create and execute machine learning models in BigQuery by using standard SQL queries."

I haven't used it in production, but I'd use it in a heartbeat if I was on BigQuery.

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

#69

Post by lysecret »

I feel like a lot of issues out of ML systems came from the fact that some person got a CSV dump of the data and then iterated for a month to build a fantastic model, which nobody knows how to integrate with the DB.

So, this is why I really like this idea and about 3 years ago I seriously thought about starting this thing as well. I went ahead and built a specific data company (so not a tooling one) and now I don't like this idea anymore.

To me this is a lot like proposing: "lets get rid of Rest Apis and Graphql and connect the frontend directly to the DB". (ignoring security issues for a bit).

In frontend: The view you like to display your data is a different one than how it should be saved. Exactly the same in ML, the view your data can be trained / predicted on is a very different than it should be stored.

They are connected, but IMO there always has to be a transformation layer. (and Python is just a much better way to do that transformation, but that's an other story)

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

#70

Post by thejansen »

Cool approach. This nicely fits in the trend of SQL-as-much-as-possible because that makes it just a tiny bit more accessible. Definitely going to play with this in the next few days. (edit:) Being able to get training data from a SQL view is by far the nicest. Keep it up!
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