Announcing the release of Spice.ai v0.4.1-alpha! ✅
This point release focuses on fixes and improvements to v0.4-alpha. Highlights include AI engine performance improvements, updates to the dashboard observations data grid, notification of new CLI versions, and several bug fixes.
A special acknowledgment to @Adm28, who added the CLI upgrade detection and prompt, which notifies users of new CLI versions and prompts to upgrade.
The Spice.ai project strives to help developers build applications that leverage new AI advances which can be easily trained, deployed, and integrated. A previous blog post introduced Spicepods: a declarative way to create AI applications with Spice.ai technology. While there are many libraries and platforms in the space, Spice.ai is focused on time-series data aligning to application-centric and frequently time-dependent data, and a Reinforcement Learning approach, which can be more developer-friendly than expensive, labeled supervised learning.
This post will discuss some of the challenges and directions for the technology we are developing.
The last post in this series, Making Apps that Learn and Adapt, described the shift from building AI/ML solutions to building apps that learn and adapt. But, how does the app learn? And as a developer, how do I teach it what it should learn?
We are excited to announce the release of Spice.ai v0.4-alpha! 🏄♂️
Highlights include support for authoring reward functions in a code file, the ability to specify the time of recommendation, and ingestion support for transaction/correlation ids. Authoring reward functions in a code file is a significant improvement to the developer experience than specifying functions inline in the YAML manifest, and we are looking forward to your feedback on it!
If you are new to Spice.ai, check out the getting started guide and star spiceai/spiceai on GitHub.
In the Spice.ai announcement blog post, we shared some of the inspiration for the project stemming from challenges in applying and integrating AI/ML into a neurofeedback application. Building upon those ideas, in this post, we explore the shift in approach from a focus of data science and machine learning (ML) to apps that learn and adapt.
