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Could Someone Give Me Guidance on Integrating KDB+/q with Machine Learning?
Hello there,
Right now I am working on a project where I need to integrate KDB+/q with machine learning models. Specifically, I am interested in using KDB+/q to preprocess data, train models, and serve predictions.
I have already explored some basic examples and tutorials, but I am looking for more in-depth resources or best practices for integrating KDB+/q with popular machine learning frameworks such as TensorFlow or PyTorch.
Also, I am curious about any experiences or tips that the community can share regarding performance optimization when using KDB+/q with large-scale machine learning models and datasets.
For reference I have taken help from this: https://stackoverflow.com/questions/47354958/connect-api-to-kdb-databaseinfo
If anyone has worked on similar projects or has any resources to recommend, I would greatly appreciate your insights.
Thankyou in advance.
stackoverflow.com
simple question - I've successfully connected to the Coinbase API using Python 3.6 and receiving BTC buy/sell prices in my console. I'd like to connect this to a kdb database and start creating a ...
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1 Reply
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There are a lot of resources here: https://code.kx.com/q/ml/
Note: These show use of embedPy which is still usable but is not being actively updated. These can be however be migrated to use the current Python interface PyKX which includes notes on upgrading from embedPy.
Related to the question to you linked to you may be interested in this Websocket example interfacing with coinapi.io:
code.kx.com
Machine learning | kdb+ and q documentation - Kdb+ and q documentation
Machine-learning capabilities are at the heart of future technology development at KX. Libraries are added here as they are released. Libraries are released under the Apache 2 license, and are free for all use cases, including 64-bit and commercial use.
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