For those of you who missed the links to the examples:<p>> Try:<p>> ServerOps sample - a more in-depth version of the quickstart you just completed, using CPU metrics from your own machine<p>> Gardener - Intelligently water a simulated garden<p>> Trader - a basic Bitcoin trading bot<p>- <a href="https://github.com/spiceai/samples/tree/trunk/serverops/README.md" rel="nofollow">https://github.com/spiceai/samples/tree/trunk/serverops/READ...</a><p>- <a href="https://github.com/spiceai/samples/tree/trunk/gardener/README.md" rel="nofollow">https://github.com/spiceai/samples/tree/trunk/gardener/READM...</a><p>- <a href="https://github.com/spiceai/quickstarts/tree/trunk/trader/README.md" rel="nofollow">https://github.com/spiceai/quickstarts/tree/trunk/trader/REA...</a>
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.<p>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.<p>Here’s an example (trying to guess what it does, could be way off):<p>Imagine you had a time series of the temperature in your room every day and when your AC engages<p>If you had a time series ML engine, it could optimize when the AC turns on and off<p>This would reduce your energy usage by not overcooling the room at the end of the day as the temp drops naturally<p>See how that format works? Situation without your thing. What your thing can do. Real world benefit user gets from using your thing.
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...
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:<p>- are they talking about prediction? I assume yes because they talk about time series but it's not explicitly stated<p>- how does it compare quality wise to Amazon forecast (and the other cloud vendors services)?<p>By the way: here's the spiel for Forecast:<p>"Amazon Forecast
Accurate time-series forecasting service, based on the same technology used at Amazon.com, no machine learning experience required"<p>That makes sense and is not ambiguous.
Congratulations on the launch!<p>You seem to be using Go as well as Python for your project. Are you calling models written in python using Go?<p>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.
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)?
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.