Nice, congrats on launching! I'm excited to try it out.
Curious - what if there's a transformation that happens with data from the data warehouse but can't be performed with SQL (such as a Python script)? Is there a way to send that data back into the integrations that you support? Or would be it best to push back into the data warehouse and use Census from there?
Aka, can you only transform with Census using SQL? Or other languages as well?
One of the things that has deterred me from SageMaker is how expensive it can be for a side project. Real-time endpoints start at $40-$50 per month, which would be a bit too much for a low-budget project on the side. I love the idea of using an open-source alternative, but I noticed that all of the systems combined for Cortex would be a bit more expensive. Do you have any tips on how to keep a model deployed cheaply for a side project using Cortex? Id be fine with a little bit of latency on the first request, similar to how Heroku's free dynos work.
As a data scientist, I found that a drop in metrics was just as often due to a data pipeline issue as it was an actual business problem. This unfortunately causes business users to lose trust in the metrics quickly. How do you plan to differentiate between those two root causes of metric changes?
Neat! Congrats on the launch - the demo is very helpful to understand the product. Having consumed long, painful PDF data dictionaries in the past, this is a big breath of fresh air. Excited to see where Syndetic goes!
For me, the most painful part of working with 3rd party data was actually figuring out the "match rate" to internal data. For example, you might be a consumer-facing company who hopes to add more context to your internal data by pulling in 3rd party information for existing clients. To match your internal data to a 3rd party dataset, you usually match on some hashed email (or similar identifier) to see what percentage of your consumer records will be available in the 3rd party dataset. Have you thought about something like that with your tool? Maybe you can upload a sample of hashed emails and see how different match rates pan out.
Very cool - congrats on the launch! I like the docker deploy command that you posted on your landing page. Tried that out and it is super easy to get up and running.
Do you have a sample dataset to feed into our local environment or demo environment to test out the UI? Id love to poke around a bit before deploying to Heroku and setting it up on a site.
Curious - what if there's a transformation that happens with data from the data warehouse but can't be performed with SQL (such as a Python script)? Is there a way to send that data back into the integrations that you support? Or would be it best to push back into the data warehouse and use Census from there?
Aka, can you only transform with Census using SQL? Or other languages as well?