Luke Kim

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.

As a developer, I've followed the AI/ML space with keen interest and been impressed with the advances and announcements that only seem to be increasing. stateof.ai recently published its 2021 report, and once again, it's been another great year of progress. At the same time, it's still more challenging than ever for mainstream developers to integrate AI/ML into their applications. For most developers, where AI/ML is not their full-time job, and without the support of a dedicated ML team, creating and developing an intelligent application that learns and adapts is still too hard.

Most solutions on the market, even those that claim they are for developers, focus on helping make ML easier instead of making it easier to build applications. These solutions have been great for advancing ML itself but have not helped developers leverage ML in their apps to make them intelligent. Even when a developer successfully integrates ML into an application, it might make that application smart, but often does not help the app continue to learn and adapt over time.

Traditionally, the industry has viewed AI/ML as separate from the application. A pipeline, service, or team is provided with data, which trains on that data, and can then provide answers or insights. These solutions are often created with a waterfall-like approach, gathering and defining requirements, designing, implementing, testing, and deploying. Sometimes this process can take months or even years.

With Spice.ai, we propose a new approach to building applications. By bringing AI/ML alongside your compute and data and incorporating it as part of your application, the app can incrementally adopt recommendations from the AI engine and in addition the AI engine can learn from the application's data and actions. This approach shifts from waterfall-like to agile-like, where the AI engine ingests streams of application and external data, along with the results of the application's actions, to continuously learn. This virtuous feedback cycle from the app to the AI engine and back again enables the app to get smarter and adapt over time. In this approach, building your application is developing the ML.

Being part of the application is not just conceptual. Development teams deploy the Spice.ai runtime and AI engine with the application as a sidecar or microservice, enabling the app services and runtime to work together and for data to be kept application local. A developer teaches the AI engine how to learn by defining application goals and rewards for actions the application takes. The AI Engine observes the application and the consequences of its actions, which feeds into its experience. As the AI engine learns, the application can adapt.

As developers shift from thinking about disparate applications and ML to building applications where AI that learns and adapts is integrated as a core part of the application logic, a new class of intelligent applications will emerge. And as technical talent becomes even more scarce, applications built this way will be necessary, not just to be competitive but to be even built at all.

In the next post, I'll discuss the concept of Spicepods, bundles of configuration that describes how the application should learn, and how the Spice.ai runtime hosts and uses them to help developers make applications that learn.

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. 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

We are excited to announce the release of Spice.ai v0.3.1-alpha! 🎃

This point release focuses on fixes and improvements to v0.3-alpha. Highlights include the ability to specify both seed and runtime data, to select custom named fields for time and tags, a new spice upgrade command and several bug fixes.

A special acknowledgment to @Adm28, who added the new spice upgrade command, which enables the CLI to self-update, which in turn will auto-update the runtime.

Highlights in v0.3.1-alpha​

Upgrade command​

The CLI can now be updated using the new spice upgrade command. This command will check for, download, and install the latest Spice.ai CLI release, which will become active on it's next run.

When run, the CLI will check for the matching version of the Spice.ai runtime, and will automatically download and install it as necessary.

The version of both the Spice.ai CLI and runtime can be checked with the spice version CLI command.

Seed data​

When working with streaming data sources, like market prices, it's often also useful to seed the dataspace with historical data. Spice.ai enables this with the new seed_data node in the dataspace configuration. The syntax is exactly the same as the data syntax. For example:

dataspaces:
  - from: coinbase
    name: btcusd
    seed_data:
      connector: file
        params:
          path: path/to/seed/data.csv
      processor:
        name: csv
    data:
      connector: coinbase
        params:
          product_ids: BTC-USD
      processor:
        name: json

The seed data will be fetched first, before the runtime data is initialized. Both sets of connectors and processors use the dataspace scoped measurements, categories and tags for processing, and both data sources are merged in pod-scoped observation timeline.

Time field selectors​

Before v0.3.1-alpha, data was required to include a specific time field. In v0.3.1-alpha, the JSON and CSV data processors now support the ability to select a specific field to populate the time field. An example selector to use the created_at column for time is:

data:
  processor:
    name: csv
    params:
      time_selector: created_at

Tag field selectors​

Before v0.3.1-alpha, tags were required to be placed in a _tags field. In v0.3.1-alpha, any field can now be selected to populate tags. Tags are pod-unique string values, and the union of all selected fields will make up the resulting tag list. For example:

dataspace:
  from: twitter
  name: tweets
  tags:
    selectors:
      - tags
      - author_id
    values:
      - spice_ai
      - spicy

New in this release​

• Adds a new spice upgrade command for self-upgrade of the Spice.ai CLI.
• Adds a new seed_data node to the dataspace configuration, enabling the dataspace to be seeded with an alternative source of data.
• Adds the ability to select a custom time field in JSON and CSV data processors with the time_selector parameter.
• Adds the ability to select custom tag fields in the dataspace configuration with selectors list.
• Adds error reporting for AI engine crashes, where previously it would fail silently.
• Fixes the dashboard pods list from "jumping" around due to being unsorted.
• Fixes rare cases where categorical data might be sent to the AI engine in the wrong format.

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]

Announcing the release of Spice.ai v0.2.1-alpha! 🚚

This point release focuses on fixes and improvements to v0.2-alpha. Highlights include the ability to specify how missing data should be treated and a new production mode for spiced.

This release supports the ability to specify how the runtime should treat missing data. Previous releases filled missing data with the last value (or initial value) in the series. While this makes sense for some data, i.e., market prices of a stock or cryptocurrency, it does not make sense for discrete data, i.e., ratings. In v0.2.1, developers can now add the fill parameter on a dataspace field to specify the behavior. This release supports fill types previous and none. The default is previous.

Example in a manifest:

dataspaces:
  - from: twitter
    name: tweets
    fields:
      - name: likes
        fill: none # The new fill parameter

spiced now defaults to a new production mode when run standalone (not via the CLI), with development mode now explicitly set with the --development flag. Production mode does not activate development time features, such as the Spicepod file watcher. The CLI always runs spiced in development mode as it is not expected to be used in production deployments.

New in this release​

• Adds a fill parameter to dataspace fields to specify how missing values should be treated.
• Adds the ability to specify the fill behavior of empty values in a dataspace.
• Simplifies releases with a single spiceai release instead of separate spice and spiced releases.
• Adds an explicit development mode to spiced. Production mode does not activate the file watcher.
• Fixes a bug when the pod parameter epoch_time was not set which would cause data not to be sent to the AI engine.
• Fixes a bug where the User-Agent was not set correctly from CLI calls to api.spicerack.org

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]

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