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.

Highlights in v0.2-alpha​

Tagged data​

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.

Streaming 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.

Interpreted data​

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.

New in this release​

• Adds core runtime and AI engine tagged data support
• Adds tagged data support to the CSV processor
• Adds streaming data support to the engine and data connectors
• Adds a new JSON data processor for ingesting JSON data
• Adds a new Twitter data connector with JSON processor support
• Adds a new /pods//dataspaces API
• Adds support for using interpretations in reward functions Learn more.
• Adds support for downloading zipped pods from the spicerack.org registry
• Adds support for adding data along with the pod manifest when adding a pod from the spicerack.org registry
• Adds basic /pods//diagnostics API
• Fixes pod period, interval, and granularity not being correctly set when trying to use a "d" format
• Fixes the color scheme of action counts in the dashboard to improve readability

Resources​

• Getting started with Spice.ai
• Documentation
• Quickstarts and Samples

Community​

Spice.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!

• Discord: https://discord.gg/kZnTfneP5u
• Reddit: https://www.reddit.com/r/spiceai
• Twitter: @spice_ai
• Email: [email protected]

AI has recently seen some impressive advances, like with OpenAI Codex and DeepMind AlphaFold 2. And at the same time, for most developers, leveraging AI to create intelligent applications is still way too hard. The Data Science Hierarchy of Needs pyramid from 2017 still illustrates it well; there are too many unmet needs in applying ML in applications.

We faced the same AI development challenges many developers do, even though we had years of engineering experience at Microsoft and GitHub, there was too much to learn and build. And we simply didn't have the time, resources, or tools to learn and utilize AI effectively in the project. After experiencing this pain ourselves, we saw an opportunity to make it better for everyone.

Today, we are making Spice.ai available on GitHub, a new open source project that helps developers use deep learning to create intelligent applications. We're looking for feedback on the direction. It's not finished, in fact, we only started this summer, and we invite you to try out the alpha.

Figure 1. Adding a Spice.ai pod, training and getting a recommendation in three commands

Like many developer stories, it all started with a side-project. We were interested in neurofeedback, a type of biofeedback therapy that reinforces healthy brain function but can cost up to $15,000. We wanted to make it accessible to more people, so we set out to build a system that leverages AI to deliver neurofeedback more cost-effectively. Using AI for the application was much more challenging than expected, and this sparked the inspiration for Spice.ai.

In the neurofeedback project, we worked with brain activity EEG data - time series data. We realized that time series data applies to many domains, from health and biometrics to finance, sales, logistics, security, IoT, and application monitoring. The amount of time series data in these fields is growing exponentially, and extracting insights from this data to make more intelligent software will determine the success of the next generation of applications.

We also realized that handling time series data is often sensitive, such as with health, financial, and security data. Instead of sending all data into a 3rd-party AI service, we needed the choice to bring the AI runtime to wherever our data and compute lived, either in the cloud, on-premises or on edge devices.

Spice.ai - a modern development experience and open source runtime for deep learning on time series data​

Spice.ai is an open source, portable runtime for training and using deep learning on time series data. It's written in Golang and Python and runs as a container or microservice with applications calling a simple HTTP API. It's deployable to any public cloud, on-premises, and edge.

The vision for Spice.ai is to make creating intelligent applications as easy as possible for developers in their development environment of choice. Spice.ai brings AI development to their editor in any language or framework with a fast, iterative, inner development loop, continuous-integration (CI), and continuous-deployment (CD) workflows.

The Spice.ai runtime also includes a library of community-driven components for streaming and processing time series data, enabling developers to quickly and easily combine data with learning to create intelligent models.

Developers can write easy-to-understand and re-useable, "pods," with manifests that connect these data components with a simple definition of the learning environment. These pods also serve as a package for the resulting trained model.

Modern developers build together with the community by leveraging registries such as npm, NuGet, and pip. The registry for sharing and using pods is spicerack.org. As the community shares more and more pods, developers can quickly build upon each others' work, initially by sharing manifests and eventually by reusing fully-trained models.

Applying Spice.ai to real-world problems​

We are currently piloting Spice.ai with several companies to create the next generation of modern applications, such as optimizing in-store pickups for a large online retailer or scheduling optimizations for healthcare workers and resources. We've already seen some cool use cases, including suspicious login detection, intelligent cloud-spend analysis, and order routing for a food delivery app.

Learn more and contribute​

Building intelligent apps that leverage AI is still way too hard, even for advanced developers. Our mission is to make this as easy as creating a modern web page.

This mission is a huge undertaking and Spice.ai v0.1-alpha has many gaps, including limited deep learning algorithms and training scale, streaming data, simulated environments, and offline learning modes. Pods aren't searchable or even listed on spicerack.org yet. But if the vision resonates with you, join us! Our Spice.ai Roadmap is public, and now that we have launched, the project and work are open for collaboration.

If you are interested in partnering, we'd love to talk. Try out Spice.ai, email us "hey," get in touch on Discord, or reach out on Twitter.

We are just getting started! 🚀

Luke, Phillip, and Lane - Spice.ai project founders