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)?
Show HN: PostgresML, now with analytics and project management
11–20 of 76 posts
Re: Show HN: PostgresML, now with analytics and project management
#12Very cool! Will probably use it soon.
Re: Show HN: PostgresML, now with analytics and project management
#13What 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.
You might want to use a different elephant logo... the branding implies that it is an official part of the project.
E.g. Postico uses an elephant but not the _same_ elephant - https://eggerapps.at/postico/
Re: Show HN: PostgresML, now with analytics and project management
#14Do you plan on adding support for managed PostgreSQL services like RDS in the future?
Re: Show HN: PostgresML, now with analytics and project management
#15Very cool! Will probably use it soon.
Thanks! We’d still consider this an early stage project and would love your feedback for which features to prioritize. Our roadmap is only getting longer…
Re: Show HN: PostgresML, now with analytics and project management
#16Hello
really nice !
Can you explane the differences with https://madlib.apache.org/ ? Wouldnt an OLAP db better suited than pg for this kind of workload ?
Does being a postgreSQL module make it compatible with citus, greemplum or timescale ?
Re: Show HN: PostgresML, now with analytics and project management
#17No post body was provided.
Re: Show HN: PostgresML, now with analytics and project management
#18Do you plan on adding support for managed PostgreSQL services like RDS in the future?
We can’t control the extensions RDS allows to be installed, and they are historically conservative. Lev and I do have some fairly extensive experience with replication patterns to Postgres instances running in EC2. Foreign data wrappers are also an option, and depending on workload may be a good horizontal scaling strategy in addition.
Re: Show HN: PostgresML, now with analytics and project management
#19Earlier quoted context omitted.
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.
You might want to use a different elephant logo... the branding implies that it is an official part of the project. E.g. Postico uses an elephant but not the _same_ elephant - https://eggerapps.at/postico/
No post body was provided.
Re: Show HN: PostgresML, now with analytics and project management
#20How 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 a few right now and can add more. The next thing we may build could be a configuration wizard to help make these decisions easy based on some guided data analysis.