Spice.ai – Open-source, time series AI for developers | Hacker News Reader

Spice.ai – Open-source, time series AI for developers

blog.spiceai.org

160 pointsprosim39 comments

For those of you who missed the links to the examples:

> Try:

> ServerOps sample - a more in-depth version of the quickstart you just completed, using CPU metrics from your own machine

> Gardener - Intelligently water a simulated garden

> Trader - a basic Bitcoin trading bot

- https://github.com/spiceai/samples/tree/trunk/serverops/READ...

- https://github.com/spiceai/samples/tree/trunk/gardener/READM...

- https://github.com/spiceai/quickstarts/tree/trunk/trader/REA...

Echoing what everyone else is saying - I have no idea what this does. Maybe I’m not in the target market and I’d jump on it if I were, but real world examples would help.

And I don’t mean real world examples like you’ve listed. Those are just names of domains (neurofeedback, order fulfillment). I can list domains too (accounting, genomics). Give me a case study of what your thing does, with the real world payoff.

Here’s an example (trying to guess what it does, could be way off):

Imagine you had a time series of the temperature in your room every day and when your AC engages

If you had a time series ML engine, it could optimize when the AC turns on and off

This would reduce your energy usage by not overcooling the room at the end of the day as the temp drops naturally

See how that format works? Situation without your thing. What your thing can do. Real world benefit user gets from using your thing.

Thanks for taking the time to write up this feedback. Yes it's obvious now we need to improve the examples, and I like your structure. Perhaps you can help us.

Would love to chat more.

PM me on Twitter at https://twitter.com/0xLukeKim if you are interested.

A minimal example would really help me figure out what this actually does.

Yes, it needs a quick start somewhere.

Try out one of the quickstarts using Github Codespaces. No setup required :)

https://github.com/spiceai/spiceai#getting-started-with-code...

I'm having trouble understanding what the goal is of this as well. It seems like the quick summary would be "ML-based forecasting/prediction in a box" but the readme is making all of these broad claims...

We're looking to make timeseries AI easier for developers to integrate into their applications by providing tooling and patterns that are familiar to them. We're a group of devs that were looking to add intelligence to one of our projects, but struggling with existing tooling. We wanted to use patterns that were more familiar to us as devs, like a quick debugging loop, easily consumed packages, etc.

Surely the context for the time series matters? Not sure good outcomes will follow from people blindly applying black boxes.

Note: has nothing to do with the long-standing ecosystem of circuit simulators, as far as I can tell. Although, hey, try pointing it at some transients, maybe we can have AIs design circuits, that could be nice.

Two questions:

- are they talking about prediction? I assume yes because they talk about time series but it's not explicitly stated

- how does it compare quality wise to Amazon forecast (and the other cloud vendors services)?

By the way: here's the spiel for Forecast:

"Amazon Forecast Accurate time-series forecasting service, based on the same technology used at Amazon.com, no machine learning experience required"

That makes sense and is not ambiguous.

It would also be nice to have some sense of what the underlying algorithms are.

Hello Gimpei. Spice.ai currently supports two algorithms, Vanilla Policy Gradient and Deep Q-Learning. It provides an interface to plug in your own algorithms, though. We're looking to add more to it as we go along.

You can find more info at https://docs.spiceai.org/deep-learning-ai/

Can you elaborate more on why you choose those? . More specifically: Did you benchmark them using known datasets? (Against other algorithms?)

Also, where do you store the training data?

We chose them because they were fairly straightforward to implement and different enough from one another that we could ensure our interface generalized well.

Re benchmarking - at this point we're looking to show directionality, not necessarily blinding speed. We intend to get the tooling feeling right, then work to optimize perf.

Right now, training data comes from the local disk, InfluxDB, or can be piped in from your application via our API. We're looking to build out a set of community-driven components for streaming and processing data. You can learn more about that here - https://github.com/spiceai/data-components-contrib

We'd love for you to contribute!

So I am not sure that I understand you. Why would you even implement a forecasting algorithm and not use open source one. (Unless I am missing something).

Also, how do you plan to verify that the algorithm works?

Note, that your customers would need to make critical business decisions based on this software, so I would refrain doing clean room impl of the forecasting algorithm.

I think you are going to confuse people by framing this as "time series" and then focussing on reinforcement learning. A lot/most of your framing is around RL.

from a first glance and a read through your roadmap, this does not feel suitable for people who know what they are doing with RL. It also does not feel suitable for people who don't know what they are doing with RL.

How about for people who know what they are doing with time series?

reinforcement learning deals with sequential decision making to maximize future rewards, time series data is any data over time

you should be more clear in your title because the two domains are very different

Here is that part of the repo:

https://github.com/spiceai/spiceai/blob/trunk/ai/src/algorit...

It’s unclear to me what all this does from the GitHub repository

Congratulations on the launch!

You seem to be using Go as well as Python for your project. Are you calling models written in python using Go?

It's rare to see Go used in ML projects(Perhaps lack of batteries like Numpy,Pandas etc.), Which is a shame because I think Go is a perfect replacement for Python and it helps to build production ready ML applications off the bat without the performance limitations of Python.

Thank you! Yes, that's what we are currently doing, however the long term direction is to move as much of the codebase to Go/Rust as possible to be portable across hosting environments as possible.

Checkout spago[1], They're converting python NLP models to pure Go.

[1] https://github.com/nlpodyssey/spago

How does it perform with live data? Can I feed a continuous stream of data and it recommends the next action? Can I limit the dataset that is taken into account (look-back amount)?

nslookup spiceai.org Server: 8.8.8.8 Address: 8.8.8.8#53

Non-authoritative answer: ** Can't find spiceai.org: No answer

docs.spiceai.org and blog.spiceai.org do work.

Need a tldr for this vs loudml

I know this may be a bit pedantic, and from a marketing perspective I'm sure everyone is telling you that "you have to market it as AI!", but my pet peeves is that the phrase "time series AI" doesn't make any sense in English. "ML for time series data" makes much more sense to me, is valid English, and from your post sounds like what you're actually doing.

I'm still waiting for quantum blockchain AI

Pfft that's so 2019, it's gotta be IoT cloud edge AI these days.

...because what really matters to most software devs is apparently increasing their job security by pushing processing and energy requirements onto the customer while roping them into a monthly support contract so they can be milked indefinitely.

Instead of a time series it computes them all at once

Thanks for taking the time to write up this feedback. Great point. We've been coding for three months, and are still figuring out exactly how to message the project.

If you don't even know what you are making, why would someone else use it?

Fitting username.

It's very difficult to succinctly explain things in a way that is accessible to people who don't have any context.

It's difficult yes. But for the right price, I can assist. Feel free to ping me.

Unrelated to the programme, but I wonder if the naming is related to "Dune" at all

To me, clearly is a play on mentats making first, second, and third-order predictions of the future, so yes.

Too bad the Butlerian Jihad forbids it XD

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