haardvark5001

We chose them because they were fairly straightforward to implement and different enough from one another that we could ensure our interface generalized well.

Re benchmarking - at this point we're looking to show directionality, not necessarily blinding speed. We intend to get the tooling feeling right, then work to optimize perf.

Right now, training data comes from the local disk, InfluxDB, or can be piped in from your application via our API. We're looking to build out a set of community-driven components for streaming and processing data. You can learn more about that here - https://github.com/spiceai/data-components-contrib

We'd love for you to contribute!

Try out one of the quickstarts using Github Codespaces. No setup required :)

https://github.com/spiceai/spiceai#getting-started-with-code...

We're looking to make timeseries AI easier for developers to integrate into their applications by providing tooling and patterns that are familiar to them. We're a group of devs that were looking to add intelligence to one of our projects, but struggling with existing tooling. We wanted to use patterns that were more familiar to us as devs, like a quick debugging loop, easily consumed packages, etc.

Hello Gimpei. Spice.ai currently supports two algorithms, Vanilla Policy Gradient and Deep Q-Learning. It provides an interface to plug in your own algorithms, though. We're looking to add more to it as we go along.

You can find more info at https://docs.spiceai.org/deep-learning-ai/