We are excited to announce the release of Spice.ai v0.2-alpha! 🎉
This release is the first major version since the initial v0.1 announcement and includes significant improvements based upon community and customer feedback. If you are new to Spice.ai, check out the getting started guide and star spiceai/spiceai on GitHub.
In the first release, the runtime and AI engine could only ingest numerical data. In v0.2, tagged data is accepted and automatically encoded into fields available for learning. For example, it's now possible to include a "liked" tag when using tweet data, automatically encoded to a 0/1 field for training. Both CSV and the new JSON observation formats support tags. The v0.3 release will add additional support for sets of categorical data.
Previously, the runtime would trigger each data connector to fetch on a 15-second interval. In v0.2, we upgraded the interface for data connectors to a push/streaming model, which enables continuous streaming data into the environment and AI engine.
Spice.ai works together with your application code and works best when it's provided continuous feedback. This feedback could be from the application itself, for example, ratings, likes, thumbs-up/down, profit from trades, or external expertise. The interpretations API was introduced in v0.1.1, and v0.2 adds AI engine support providing a way to give meaning or an interpretation of ranges of time-series data, which are then available within reward functions. For example, a time range of stock prices could be a "good time to buy," or perhaps Tuesday mornings is a "good time to tweet," and an application or expert can teach the AI engine this through interpretations providing a shortcut to it's learning.
/pods//dataspaces API/pods//diagnostics APISpice.ai started with the vision to make AI easy for developers. We are building Spice.ai in the open and with the community. Reach out on Discord or by email to get involved. We will also be starting a community call series soon!