
Machine Learning as a Service - gsharma
http://about.wise.io/
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jgalt212
I dunno. Machine Learning as a Service seems like a tough thing to monetize,
as most machine learning in practice involves a lot of tweaking which would
then imply that practitioners would like to go further down the stack to work
directly with an R, Python, ?? module, look at its code, see where it's
failing, working etc.

I do like Machine Learning as a Service as a loss leader. e.g. customer walks
up to the door, can't really get the problem cracked with an out of the box
solution, but instead you sell/him her on an expensive long-term consulting
project. i.e. the IBM Model.

Does anyone know how one of the pioneers in the segment, Numenta, is fairing?
They've been around for a while and seem to have recently changed their name
to Grok Solutions.

Deep Learning as a service seems like something that could work as their are
less knobs for the user to fiddle with. That being said, it does not seem like
Deep Learning is quite there yet.

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hkmurakami
I know of at least one Berkeley ML PhD. that was working on a startup that
could have easily used the slogan, "Machine Learning as a Service".

I have to wonder how founders/founders-in-the-making react when faculty
members from their alma mater, from their own department no less, enter their
space. Must be a little bit like having Google enter your niche.

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_pmf_
> I know of at least one Berkeley ML PhD. that was working on a startup that
> could have easily used the slogan

Out of interest: what is/was the companies name?

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hkmurakami
he was still building the product demo when I last talked to him so
unfortunately I don't know the answer

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ozten
To keep ML fast and cheap, you want to keep compute as close to data as
possible.

I don't think a web service would be broadly applicable. Perhaps in certain
domains it would make sense, but bandwidth costs and duration would be a huge
factor in most solutions.

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jaytaylor
I completely disagree. I've used siftscience (<http://siftscience.com>) and
the service is remarkably good. I admit there are some cases where it would be
impractical. However, in my experience ML-as-a-service has been pragmatic and
I've seen it lead to very favorable outcomes with minimal effort from the
client.

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raylu
So... instead of disagreeing out of hand, how did you overcome the fact that
you had to pay for bandwidth between your terabytes of data and the
computation centers?

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jaytaylor
Terabytes are cheap compared to my time. I've also never required that much
training data, so perhaps your use case does not lend itself to these
services. I've built automated ML model training systems myself with TreeNet
and Mahout, as well as used API-based ML systems, and I'm comfortable saying
there is a strong case to be made for services which save much unnecessary
effort.

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raylu
_Storing_ terabytes is cheap. Terabytes of _bandwidth_ is very very expensive.

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neeleshs
Some old links on the cost of terabytes of bandwidth
[http://josephscott.org/archives/2009/01/how-much-does-one-
te...](http://josephscott.org/archives/2009/01/how-much-does-one-terabyte-of-
bandwidth-cost/)

~~~
raylu
And that assumes you're willing to host with those providers, many of whom
don't provide computing power. Also, the machine learning service you are
using has to pay again for that bandwidth too and that cost gets passed along
to you.

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curiousRevision
What IP are they patenting? It sounds like they just implemented random
forests in C++ in a way that avoided data copy, which is obviously what you'd
want to do for performant non-parametric machine learning... So what's
patentable here?

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Groxx
How does this compare to, say, the Google Prediction Service[1] ? The example
use-cases sound similar-ish to my layman interpretation. I've never used a ML
service though, so honestly I have no idea what to expect or look for.

Obviously this offers an on-site option that Google does not, which might open
up other realms of options. I'm mostly curious in how / how well / what range
of problems they're capable of.

[1] <https://developers.google.com/prediction/>

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hayksaakian
The OP's service has a better chance of staying around when it comes time for
spring cleaning.

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Groxx
True :) Though IIRC Google's prediction API has been paid from day 1, so it
might stick its neck out a bit less than e.g. Reader.

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piokuc
It would be interesting to know how popular Google's prediction API actually
is. They don't show off any testimonials or customers' success stories. The
traffic on their forum seems rather moderate:
[https://developers.google.com/prediction/docs/general_discus...](https://developers.google.com/prediction/docs/general_discussion_forum)
Regardless, I do believe MLaaS makes sense, not necessarily as a fully
automated black box thingy, it could and probably should be backed by a
consulting service - this is, I think, where startups in this space will have
an edge over Google.

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noelwelsh
Couple of points:

\- Benchmarking against Weka, R, and Python is not exactly pitting your
product against stiff competition. Skytree (skytree.net) is another company in
the same space with the same focus. Benchmarking against them would be
interesting.

\- I thought it was amusing that in a company of < 20 people the five founders
thought it necessary to adopt such grandiose titles: CEO (fine), CTO, Director
of Engineering, Chief Scientist, Director of Data Science (exactly how are the
hairs split between these four?)

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piokuc
I think the second point was completely unnecessary. I didn't find it amusing
or funny, just sarcastic. Yes, sarcastic, but not funny. I guess you consider
wise.io your competition, don't you?

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hermaj
Speaking as someone with some years experience in ML to me there is a world of
difference between the front page for wise.io and mynaweb.com (presuming these
are the competing sites).

The Myna page explicitly states it uses multi armed bandit algorithms, the
algorithm fits the properties of the following claims. Wise's use of 'patent-
pending', 'machine learning technology' and 'deploy machine intelligence'
gives the impression of hiding shortcomings with jargon.

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visarga
Is this algorithm (a Random Forest variant named WiseRF) difficult to tune or
easy? Does the user have to guess parameters, learning rates and such?

I've found that ML algorithms can be like race cars - you really have to know
their quirks to get performance out of them. The opposite would be analogous
to a luxury car - almost everything is taken care of by the computer - you
don't shift gears and don't open the lid.

So, is this WiseRF a race car or a luxury car?

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vladoh
I actually think that random forests are relatively difficult to tune in
comparison to some Boosting methods for example.

The typical parameters are the following: \- Number of trees \- Percentage of
data used to train each tree \- Maximal depth of the tree \- Minimum
information gain (although this can usually be set to 0 and use only the
depth) \- Minimum sample size (same as the minimal information gain) \- Number
of thresholds to try for continuous data

Tuning is also difficult because of the non deterministic nature of the
algorithm. If you compare to sets of parameters it requires more evaluation in
order to be sure if one set is better because of the better choice of the
parameters or because the algorithm chose the right data samples. This effect
decreases with the number of trees, but the training time is increased in this
case.

I think random forests are really good for very large datasets, but in my
experience for smaller datasets boosting (for example JointBoost or
GentleBoost) can give better results.

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taliesinb
Speaking of bagging: Geoff Hinton talks a bit about model averaging and has a
very stimulating pair of hypotheses related to this, which are:

a) genetic recombination in the form of sex might be "bagging across genes"
that prevents tight co-adaptation (= overfitting on the evolutionary
timescale)

b) the brain uses noisy discrete firing instead of continuous communication
because it allows for "bagging across network topologies".

The bulk of his talk is about how dropout in neural nets can be used to
accomplish some of the same performance benefits that model averaging gives on
other algorithms (at a much smaller relative computational cost).

Here's the talk: <https://www.youtube.com/watch?v=DleXA5ADG78>

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gracenotes
So, at a high level, some future version of this software will show intuitive
visualizations of learned models of the data? Or currently, at a lower level,
it seems to be implementations of standard machine algorithms with a Python
API to use them.

There seems to be an emphasis on efficiency, although I don't think that most
freely available machine learning libraries are fundamentally poorly
implemented. One problem with these libraries is that documentation can
sometimes be scarce. Another problem, for the 0.01% of companies which
actually have "big data", is that they might not scale, whatever that means.

Regardless of the library used, one of the bigger problems may be that machine
learning, if it's worth it at all, is inherently fickle and tricky. To make an
overly broad conjecture: if an externally provided machine learning solution
works well, either your data didn't require that much domain knowledge to
understand (it was "obvious") or some external/outsourced firm has a deeper
understanding of your data than you do. More of the former type of analysis
might not necessarily be a bad thing, though.

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hobbes78
One tiny bit of my MSc thesis was based on Google Prediction API and it worked
fine, apart the non-conventional URLs used that caused problems with .NET
networking.

Even in a project where ML is more important, I think it's usable...

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Rickasaurus
Interested in hearing some experience reports. Those stats look too good to be
true, and they don't mention which algorithms.

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damianeads
Hi,

Thanks for your interest. You can try it yourself by downloading a trial
edition of the software. The page <http://about.wise.io/wiserf/> mentions the
algorithm we're using.

As for the stats, you can reproduce the numbers yourself if you're interested.
The ML codes are written in C++ with a very strong emphasis on performance and
a low memory footprint.

Give it a try and let us know what you think.

Damian Eads Director of Engineering, wise.io

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theschreon
Will this software focus solely on Random Forests? Hoped to see e.g. Deep
Convolutional Neural Networks as an option :(

