A significant challenge when developing an app powered by AI is providing the machine learning (ML) engine with data in a format that it can use to learn. To do that, you need to normalize the numerical data, one-hot encode categorical data, and decide what to do with incomplete data - among other things.

This data handling is often challenging! For example, to learn from Bitcoin price data, the prices are better if normalized to a range between -1 and 1. Being close to 0 is also a problem because of the lack of precision in floating-point representations (usually under 1e-5).

As a developer, if you are new to AI and machine learning, a great talk that explains the basics is Machine Learning Zero to Hero. Spice.ai makes the process of getting the data into an AI-ready format easy by doing it for you!

What is AI-ready data?​

You write code with if statements and functions, but your machine only understands 1s and 0s. When you write code, you leverage tools, like a compiler, to translate that human-readable code into a machine-readable format.

Similarly, data for AI needs to be translated or "compiled" to be understood by the ML engine. You may have heard of tensors before; they are simply another word for a multi-dimensional array and they are the language of ML engines. All inputs to and all outputs from the engine are in tensors. You could use the following techniques when converting (or "compiling") source data to a tensor.

1. Normalization/standardization of the numerical input data. Many of the inputs and outputs in machine learning are interpreted as probability distributions. Much of the math that powers machine learning, such as softmax, tanh, sigmoid, etc., is meant to work in the [-1, 1] range.

Figure 1. Normalizing Bitcoin price data.

1. Conversion of categorical data into numerical data. For categorical data (i.e., colors such as "red," "blue," or "green"), you can achieve this through a technique called "One Hot Encoding." In one hot encoding, each possible value in the category appears as a column. The values in the column are assigned a binary value of 1 or 0 depending on whether the value exists or not.

Figure 2. A visualization of one-hot encoding.

1. Several advanced techniques exist for "compiling" this source data - this process is known in the AI world as "feature engineering." This article goes into more detail on feature engineering techniques if you are interested in learning more.

There are excellent tools like Pandas, Numpy, scipy, and others that make the process of data transformation easier. However, most of these tools are Python libraries and frameworks - which means having to learn Python if you don't know it already. Plus, when building intelligent apps (instead of just doing pure data analysis), this all needs to work on real-time data in production.

Building intelligent apps​

The tools mentioned above are not designed for building real-time apps. They are often designed for analytics/data science.

In your app, you will need to do this data compilation in real-time - and you can't rely on a local script to help process your data. It becomes trickier if the team responsible for the initial training of the machine learning model is not the team responsible for deploying it out into production.

How data is loaded and processed in a static dataset is likely very different from how the data is loaded and processed in real-time as your app is live. The result often is two separate codebases that are maintained by different teams that are both responsible for doing the same thing! Ensuring that those codebases stay consistent and evolve together is another challenge to tackle.

Spice.ai helps developers build apps with real-time ML​

Spice.ai handles the "compilation" of data for you.

You specify the data that your ML should learn from in a Spicepod. The Spice.ai runtime handles the logistics of gathering the data and compiling it into an AI-ready format.

It does this by using many techniques described earlier, such as normalization and one-hot encoding. And because we're continuing to evolve Spice.ai, our data compilation will only get better over time.

In addition, the design of the Spice.ai runtime naturally ensures that the data used for both the training and real-time cases are consistent. Spice.ai uses the same data-components and runtime logic to produce the data. And not only that, you can take this a step further and share your Spicepod with someone else, and they would be able to use the same AI-ready data for their applications.

Summary​

Spice.ai handles the process of compiling your data into an AI-ready format in a way that is consistent both during the training and real-time stages of the ML engine. A Spicepod defines which data to get and where to get it. Sharing this Spicepod allows someone else to use the same AI-ready data format in their application.

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! 🚀

Phillip

In my previous post, Teaching Apps how to Learn with Spicepods, I introduced Spicepods as packages of configuration that describe an application's data-driven goals and how it should learn from data. To leverage Spice.ai in your application, you can author a Spicepod from scratch or build upon one fetched from the spicerack.org registry. In this post, we'll walk through the creation and authoring of a Spicepod step-by-step from scratch.

As a refresher, a Spicepod consists of:

• A required YAML manifest that describes how the pod should learn from data
• Optional seed data
• Learned model/state
• Performance telemetry and metrics

We'll create the Spicepod for the ServerOps Quickstart, an application that learns when to optimally run server maintenance operations based upon the CPU-usage patterns of a server machine.

We'll also use the Spice CLI, which you can install by following the Getting Started guide or Getting Started YouTube video.

Fast iterations​

Modern web development workflows often include a file watcher to hot-reload so you can iteratively see the effect of your change with a live preview.

Spice.ai takes inspiration and enables a similar Spicepod manifest authoring experience. If you first start the Spice.ai runtime in your application root before creating your Spicepod, it will watch for changes and apply them continuously so that you can develop in a fast, iterative workflow.

You would normally do this by opening two terminal windows side-by-side, one that runs the runtime using the command spice run and one where you enter CLI commands. In addition, developers would open the Spice.ai dashboard located at http://localhost:8000 to preview changes they make.

Creating a Spicepod​

The easiest way to create a Spicepod is to use the Spice.ai CLI command: spice init <Spicepod name>. We'll make one in the ServerOps Quickstart application called serverops.

The CLI saves the Spicepod manifest file in the spicepods directory of your application. You can see it created a new serverops.yaml file, which should be included in your application and be committed to your source repository. Let's take a look at it.

The initialized manifest file is very simple. It contains a name and three main sections being:

• dataspaces
• actions
• training

We'll walk through each of these in detail, and as a Spicepod author, you can always reference the documentation for the Spicepod manifest syntax.

Authoring a Spicepod manifest​

You author and edit Spicepod manifest files in your favorite text editor with a combination of Spice.ai CLI helper commands. We eventually plan to have a VS Code extension and dashboard/portal editing abilities to make this even easier.

Adding a dataspace​

To build an intelligent, data-driven application, we must first start with data.

A Spice.ai dataspace is a logical grouping of data with definitions of how that data should be loaded and processed, usually from a single source. A combination of its data source and its name identifies it, for example, nasdaq/msft or twitter/tweets. Read more about Dataspaces in the Core Concepts documentation.

Let's add a dataspace to the Spicepod manifest to load CPU metric data from a CSV file. This file is a snapshot of data from InfluxDB, a time-series database we like.

We can see this dataspace is identified by its source hostmetrics and name cpu. It includes a data section with a file data connector, the path to the file, and a data processor to know how to process it. In addition, it defines a single measurement usage_idle under the measurements section, which is a measurement of CPU load. In Spice.ai, measurements are the core primitive the AI engine uses to learn and is always numerical data. Spice.ai includes a growing library of community contributable data connectors and data processors you can consist of in your Spicepod to access data. You can also contribute your own.

Finally, because the data is a snapshot of live data loaded from a file, we must set a Spicepod epoch_time that defines the data's start Unix time.

Now we have a dataspace, called hostmetrics/cpu, that loads CSV data from a file and processes the data into a usage_idle measurement. The file connector might be swapped out with the InfluxDB connector in a production application to stream real-time CPU metrics into Spice.ai. And in addition, applications can always send real-time data to the Spice.ai runtime through its API with a simple HTTP POST (and in the future, using Web Sockets and gRPC).

Adding actions​

Now that the Spicepod has data, let's define some data-driven actions so the ServerOps application can learn when is the best time to take them. We'll add three actions using the CLI helper command, spice action add.

And in the manifest:

Adding rewards​

The Spicepod now has data and possible actions, so we can now define how it should learn when to take them. Similar to how humans learn, we can set rewards or punishments for actions taken based on their effect and the data. Let's add scaffold rewards for all actions using the spice rewards add command.

We now have rewards set for each action. The rewards are uniform (all the same), meaning the Spicepod is rewarded the same for each action. Higher rewards are better, so if we change perform_maintenance to 2, the Spicepod will learn to perform maintenance more often than the other actions. Of course, instead of setting these arbitrarily, we want to learn from data, and we can do that by referencing the state of data at each time-step in the time-series data as the AI engine trains.

The rewards themselves are just code. Currently, we currently support Python code, either inline or in a .py external code file and we plan to support several other languages. The reward code can access the time-step state through the prev_state and new_state variables and the dataspace name. For the full documentation, see Rewards.

Let's add this reward code to perform_maintenance, which will reward performing maintenance when there is low CPU usage.

cpu_usage_prev = 100 - prev_state.hostmetrics_cpu_usage_idle
cpu_usage_new = 100 - new_state.hostmetrics_cpu_usage_idle
cpu_usage_delta = cpu_usage_prev - cpu_usage_new
reward = cpu_usage_delta / 100

This code takes the CPU usage (100 minus the idle time) deltas between the previous time state and the current time state, and sets the reward to be a normalized delta value between 0 and 1. When the CPU usage is moving from higher cpu_usage_prev to lower cpu_usage_low, its a better time to run server maintenance and so we reward the inverse of the delta. E.g. 80% - 50% = 30% = 0.3. However, if the CPU moves lower to higher, 50% - 80% = -30% = -0.3, it's a bad time to run maintenance, so we provide a negative reward or "punish" the action.

Through these rewards and punishments and the CPU metric data, the Spicepod will when it is a good time to perform maintence and be the decision engine for the ServerOps application. You might be thinking you could write code without AI to do this, which is true, but handling the variety of cases, like CPU spikes, or patterns in the data, like cyclical server load, would take a lot of code and a development time. Applying AI helps you build faster.

Putting it all together​

The manifest now has defined data, actions, and rewards. The Spicepod can get data to learn which actions to take and when based on the rewards provided.

If the Spice.ai runtime is running, the Spicepod automatically trains each time the manifest file is saved. As this happens reward performance can be monitored in the dashboard.

Once a training run completes, the application can query the Spicepod for a decision recommendation by calling the recommendations API http://localhost:8000/api/v0.1/pods/serverops/recommendation. The API returns a JSON document that provides the recommended action, the confidence of taking that action, and when that recommendation is valid.

In the ServerOps Quickstart, this API is called from the server maintenance PowerShell script to make an intelligent decision on when to run maintenance. The ServerOps Sample, which uses live data, can be continuously trained to learn and adapt even as the live data changes due to load patterns changing.

The full Spicepod manifest from this walkthrough can be added from spicerack.org using the spice add quickstarts/serverops command.

Summary​

Leveraging Spice.ai to be the decision engine for your server maintenance application helps you build smarter applications, faster that will continue to learn and adapt over time, even as usage patterns change over time.

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

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.

Highlights in v0.4.1-alpha​

AI engine performance improvements​

Overall training performance has been improved up to 13% by removing a lock in the AI engine.

In versions before v0.4.1-alpha, performance was especially impacted when streaming new data during a training run.

Dashboard Observations Datagrid​

The dashboard observations datagrid now automatically resizes to the window width, and headers are easier to read, with automatic grouping into dataspaces. In addition, column widths are also resizable.

CLI version detection and upgrade prompt​

When it is run, the Spice.ai CLI will now automatically check for new CLI versions once a day maximum.

If it detects a new version, it will print a notification to the console on spice version, spice run or spice add commands prompting the user to upgrade using the new spice upgrade command.

New in this release​

• Adds automatic resizing of the observations datagrid.
• Adds header group by dataspace to the observations datagrid.
• Adds CLI version detection and prompt for upgrade on version, run, and add commands.
• Adds Support for parsing hex-encoded times and measurements. Use the time_format of hex or prefix with 0x.
• Updates AI engine with improved training performance.
• Updates Go and NPM dependencies.
• Fixes detection of Spicepods in the Spicepods directory, and a resulting error when loading a non-Spicepod file.
• Fixes a potential "zip slip" security issue.
• Fixes an issue where the AI engine may not gracefully shutdown.

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]

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.

Time Series​

Figure 1. Time Series processing visualization: a time window is usually chosen to process part of the data stream

Time series AI has become more popular over recent years, and there is extensive literature on the subject, including time-series-focused neural networks. Research in this space points to the likelihood that there is no silver bullet, and a single approach to time series AI will not be sufficient. However, for developers, this can make building a product complex, as it comes with the challenge of exploring and evaluating many algorithms and approaches.

A fundamental challenge of time series is the data itself. The shape and length are usually variable and can even be infinite (real-time streams of data). The volume of data required is often too much for simple and efficient machine learning algorithms such as Decision Trees. This challenge makes Deep Learning popular to process such data. There are several types of neural networks that have been shown to work well with time series so let's review some of the common classes:

• Convolutional Neural Networks (CNN): CNN's can only accept data with fixed lengths: even with the ability to pad the data, this is a major drawback for time-series data as a specific time window needs to be decided. Despite this limitation, they are the most efficient network to train (computation, data needed, time) and usually the smallest storage. CNN's are very robust and used in image/video processing, making them a very good baseline to start with while also benefiting from refined and mature development over the years, such as with the very efficient MobileNet with depth-wise convolutions.
• Recurrent Neural Networks (RNN): RNNs have been researched for several decades, and while they aren't as fast to train as CNNs, they can be faster to apply as there is no need to feed a time window like CNNs if the desired input/output is in real-time (in a continuous fashion, also called 'online). RNNs are proven to be very good in some situations, and many new models are being discovered.
• Transformers: Most of the state-of-the-art results today have been made from transformers and their variations. They are very good at correlating sparse information. Popularized in the famous paper Attention is all you need, transformers are proven to be flexible with high-performance in many classes (Vision Transformers, Perceiver, etc.). They suffer the same limitation as CNNs for the length of their input (fixed at training time), but they also have a disadvantage of not scaling well with the size of the data (quadratic growth with the length of the time series). They are also the most expensive network to train in general.

While not a complete representation of classes of neural networks, this list represents the areas of the most potential for Spice.ai's time-series AI technology. We also see other interesting paradigms to explore when improving the core technology like Memory Augmented Neural Networks (MANN) or neural network-based Genetical Algorithms.

Reinforcement Learning​

Reinforcement Learning (RL) has grown steadily, especially in fields like robotics. Usually, RL doesn't require as much data processing as Supervised Learning, where large datasets can be demanding for hardware and people alike. RL is more dynamic: agents aren't trained to replicate a specific behaviors/output but explore and 'exploit' their environment to maximize a given reward.

Most of today's research is based on environments the agent can interact with during the training process, known as online learning. Usually, efficient training processes have multiple agent/environment pairs training together and sharing their experiences. Having an environment for agents to interact enables different actions from the actual historical state known as on-policy learning, and using only past experiences without an environment is off-policy learning.

Figure 2. AI training without interacting with the environment (real world nor simulation). Only gathered data is used for training.

Spice.ai is initially taking an off-policy approach, where an environment (either pre-made or given by the user) is not required. Despite limiting the exploration of agents, this aligns to an application-centric approach as:

• Creating a real-world model or environment can be difficult and expensive to create, arguably even impossible.
• Off-policy learning is normally more efficient than on-policy (time/data and computation).

The Spice.ai approach to time series AI can be described as 'Data-Driven' Reinforcement Learning. This domain is very exciting, and we are building upon excellent research that is being published. The Berkeley Artificial Intelligence Research's blog shows the potential of this field and many other research entities that have made great discoveries like DeepMind, Open AI, Facebook AI and Google AI (among many others). We are inspired and are building upon all the research in Reinforcement Learning to develop core Spice.ai technology.

If you are interested in Reinforcement Learning, we recommend following these blogs, and if you'd like to partner with us on the mission of making it easier to build intelligent applications by leveraging RL, we invite you to discuss with us on Discord, reach out on Twitter or email us.

Corentin

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?

With Spice.ai, we teach the app how to learn using a Spicepod.

Imagine you own a restaurant. You created a menu, hired staff, constructed the kitchen and dining room, and got off to a great start when it first opened. However, over the years, your customers' tastes changed, you've had to make compromises on ingredients, and there's a hot new place down the street... business is stagnating, and you know that you need to make some changes to stay competitive.

You have a few options. First, you could gather all the data, such as customer surveyss, seasonal produce metrics, and staff performance profiles. You may even hire outside consultants. You then take this data to your office, and after spending some time organizing, filtering, and collating it, you've discovered an insight! Your seafood dishes sell poorly and cost the most... you are losing money! You spend several weeks or months perfecting a new menu, which you roll out with much fanfare! And then… business is still poor. What!? How could this be? It was a data-driven approach! You start the process again. While this approach is a worthy option, it has long latency from data to learning to implementation.

Another option is to build real-time learning and adaption directly into the restaurant. Imagine a staff member whose sole job was learning and adapting how the restaurant should operate; lets name them Blue. You write a guide for Blue that defines certain goal metrics, like customer food ratings, staff happiness, and of course, profit. Blue tracks each dish served, from start to finish, from who prepared it to its temperature, its costs, and its final customer taste rating. Blue not only learns from each customer review as each dish is consumed but also how dish preparation affects other goal metrics, like profitability. The restaurant staff consults Blue to determine any adjustments to improve goal metrics as they work. The latency from data to learning, to adaption, has been reduced, from weeks or months to minutes. This option, of course, is not feasible for most restaurants, but software applications can use this approach. Blue and his instructions are analogous to the Spice.ai runtime and manifest.

In the Spice.ai model, developers teach the app how to learn by describing goals and rewarding its actions, much like how a parent might teach a child. As these rewards are applied in training, the app learns what actions maximize its rewards towards the defined goals.

Returning to the restaurant example, you can think of the Spice.ai runtime as Blue, and Spicepod manifests as the guide on how Blue should learn. Individual staff members would consult with Blue for ongoing recommendations on decisions to make and how to act. These goals and rewards are defined in Spicepods or "pods" for short. Spicepods are packages of configuration that describe the application's goals and how it should learn from data. Although it's not a direct analogy, Spicepods and their manifests can be conceptualized similar to Docker containers and Dockerfiles. In contrast, Dockerfiles define the packaging of your app, Spicepods specify the packaging of your app's learning and data.

Anatomy of a Spicepod

A Spicepod consists of:

• A required YAML manifest that describes how the pod should learn from data
• Optional seed data
• Learned model/state
• Performance telemetry and metrics

Developers author Spicepods using the spice CLI command such as with spice pod init <name> or simply by creating a manifest file such as mypod.yaml in the spicepods directory of their application.

Here's an example of the Tweet Recommendation Quickstart Spicepod manifest.

A screenshot of the Spicepod manifest for the Tweet Recommendation Quickstart

You can see the data definitions under dataspaces, the actions the application may take under actions, and their rewards when training.

In the next post, I'll walk through in detail each section of the pod manifest. In the meantime, you can review the documentation for a complete reference of the Spicepod manifest syntax.

Spicepods as packages

On disk, Spicepods are generally layouts of a manifest file, seed data, and trained models, but they can also be exported as zipped packages.

A screenshot of the Spicepod layout for the trader quickstart application

When the runtime exports a Spicepod using the spice export command, it is saved with a .spicepod extension. It can then be shared, archived, or imported into another instance of the Spice.ai runtime.

Soon, we also expect to enable publishing of .spicepods to spicerack.org, from where community-created Spicepods can easily be added to your application using spice add <pod name> (currently, only Spice AI published pods are available on spicerack.org).

Treating Spicepods as packages and enabling their sharing and distribution through spicerack.org will help developers share their "restaurant guides" and build upon each other's work, much like they do with npmjs.org or pypi.org. In this way, developers can together build better and more intelligent applications.

In the next post, we'll dive deeper into authoring a Spicepod manifest to create an intelligent application. Follow @spice_ai on Twitter to get an update when we post.

If you haven't already, read the next the first post in the series, Making Apps that Learn and Adapt.

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