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From Roberto Bentivoglio <roberto.bentivog...@radicalbit.io>
Subject Re: [DISCUSS] Flink ML roadmap
Date Thu, 02 Mar 2017 09:52:25 GMT
Hi All,

First of all I'd like to introduce myself: my name is Roberto Bentivoglio
and I'm currently working for Radicalbit as Andrea Spina (he already wrote
on this thread).
I didn't have the chance to directly contribute on Flink up to now, but
some colleagues of mine are doing that since at least one year (they
contributed also on the machine learning library).

I hope I'm not jumping into discussione too late, it's really interesting
and the analysis document is depicting really well the scenarios currently
available. Many thanks for your effort!

If I can add my two cents to the discussion I'd like to add the following:
 - it's clear that currently the Flink community is deeply focused on
streaming features than batch features. For this reason I think that
implement "Offline learning with Streaming API" is really a great idea.
 - I think that the "Online learning" option is really a good fit for
Flink, but maybe we could give at the beginning an higher priority to the
"Offline learning with Streaming API" option. However I think that this
option will be the main goal for the mid/long term.
 - we implemented a library based on jpmml-evaluator[1] and flink called
"flink-jpmml". Using this library you can train the models on external
systems and use those models, after you've exported in a PMML standard
format, to run evaluations on top of DataStream API. We don't have open
sourced this library up to now, but we're planning to do this in the next
weeks. We'd like to complete the documentation and the final code reviews
before to share it. I hope it will be helpful for the community to enhance
the ML support on Flink
 - I'd like also to mention that the Apache Beam community is thiking on a
ML DSL. There is a design document and a couple of Jira tasks for that
[2][3]

We're really keen to focus our effort to improve the ML support on Flink in
Radicalbit, we will contribute on this effort for sure on a regular basis
with our team.

Looking forward to work with you!

Many thanks,
Roberto

[1] - https://github.com/jpmml/jpmml-evaluator
[2] -
https://docs.google.com/document/d/17cRZk_yqHm3C0fljivjN66MbLkeKS1yjo4PBECHb-xA
[3] - https://issues.apache.org/jira/browse/BEAM-303

On 28 February 2017 at 19:35, Gábor Hermann <mail@gaborhermann.com> wrote:

> Hi Philipp,
>
> It's great to hear you are interested in Flink ML!
>
> Based on your description, your prototype seems like an interesting
> approach for combining online+offline learning. If you're interested, we
> might find a way to integrate your work, or at least your ideas, into Flink
> ML if we decide on a direction that fits your approach. I think your work
> could be relevant for almost all the directions listed there (if I
> understand correctly you'd even like to serve predictions on unlabeled
> data).
>
> Feel free to join the discussion in the docs you've mentioned :)
>
> Cheers,
> Gabor
>
>
> On 2017-02-27 18:39, Philipp Zehnder wrote:
>
> Hello all,
>>
>> I’m new to this mailing list and I wanted to introduce myself. My name is
>> Philipp Zehnder and I’m a Masters Student in Computer Science at the
>> Karlsruhe Institute of Technology in Germany currently writing on my
>> master’s thesis with the main goal to integrate reusable machine learning
>> components into a stream processing network. One part of my thesis is to
>> create an API for distributed online machine learning.
>>
>> I saw that there are some recent discussions how to continue the
>> development of Flink ML [1] and I want to share some of my experiences and
>> maybe get some feedback from the community for my ideas.
>>
>> As I am new to open source projects I hope this is the right place for
>> this.
>>
>> In the beginning, I had a look at different already existing frameworks
>> like Apache SAMOA for example, which is great and has a lot of useful
>> resources. However, as Flink is currently focusing on streaming, from my
>> point of view it makes sense to also have a streaming machine learning API
>> as part of the Flink ecosystem.
>>
>> I’m currently working on building a prototype for a distributed streaming
>> machine learning library based on Flink that can be used for online and
>> “classical” offline learning.
>>
>> The machine learning algorithm takes labeled and non-labeled data. On a
>> labeled data point first a prediction is performed and then this label is
>> used to train the model. On a non-labeled data point just a prediction is
>> performed. The main difference between the online and offline algorithms is
>> that in the offline case the labeled data must be handed to the model
>> before the unlabeled data. In the online case, it is still possible to
>> process labeled data at a later point to update the model. The advantage of
>> this approach is that batch algorithms can be applied on streaming data as
>> well as online algorithms can be supported.
>>
>> One difference to batch learning are the transformers that are used to
>> preprocess the data. For example, a simple mean subtraction must be
>> implemented with a rolling mean, because we can’t calculate the mean over
>> all the data, but the Flink Streaming API is perfect for that. It would be
>> useful for users to have an extensible toolbox of transformers.
>>
>> Another difference is the evaluation of the models. As we don’t have a
>> single value to determine the model quality, in streaming scenarios this
>> value evolves over time when it sees more labeled data.
>>
>> However, the transformation and evaluation works again similar in both
>> online learning and offline learning.
>>
>> I also liked the discussion in [2] and I think that the competition in
>> the batch learning field is hard and there are already a lot of great
>> projects. I think it is true that in most real world problems it is not
>> necessary to update the model immediately, but there are a lot of use cases
>> for machine learning on streams. For them it would be nice to have a native
>> approach.
>>
>> A stream machine learning API for Flink would fit very well and I would
>> also be willing to contribute to the future development of the Flink ML
>> library.
>>
>>
>>
>> Best regards,
>>
>> Philipp
>>
>> [1] http://apache-flink-mailing-list-archive.1008284.n3.nabble.
>> com/DISCUSS-Flink-ML-roadmap-td16040.html <http://apache-flink-mailing-l
>> ist-archive.1008284.n3.nabble.com/DISCUSS-Flink-ML-roadmap-td16040.html>
>> [2] https://docs.google.com/document/d/1afQbvZBTV15qF3vobVWUjxQc
>> 49h3Ud06MIRhahtJ6dw/edit#heading=h.v9v1aj3xosv2 <
>> https://docs.google.com/document/d/1afQbvZBTV15qF3vobVWUjxQ
>> c49h3Ud06MIRhahtJ6dw/edit#heading=h.v9v1aj3xosv2>
>>
>>
>> Am 23.02.2017 um 15:48 schrieb Gábor Hermann <mail@gaborhermann.com>:
>>>
>>> Okay, I've created a skeleton of the design doc for choosing a direction:
>>> https://docs.google.com/document/d/1afQbvZBTV15qF3vobVWUjxQc
>>> 49h3Ud06MIRhahtJ6dw/edit?usp=sharing
>>>
>>> Much of the pros/cons have already been discussed here, so I'll try to
>>> put there all the arguments mentioned in this thread. Feel free to put
>>> there more :)
>>>
>>> @Stavros: I agree we should take action fast. What about collecting our
>>> thoughts in the doc by around Tuesday next week (28. February)? Then decide
>>> on the direction and design a roadmap by around Friday (3. March)? Is that
>>> feasible, or should it take more time?
>>>
>>> I think it will be necessary to have a shepherd, or even better a
>>> committer, to be involved in at least reviewing and accepting the roadmap.
>>> It would be best, if a committer coordinated all this.
>>> @Theodore: Would you like to do the coordination?
>>>
>>> Regarding the use-cases: I've seen some abstracts of talks at SF Flink
>>> Forward [1] that seem promising. There are companies already using Flink
>>> for ML [2,3,4,5].
>>>
>>> [1] http://sf.flink-forward.org/program/sessions/
>>> [2] http://sf.flink-forward.org/kb_sessions/experiences-with-str
>>> eaming-vs-micro-batch-for-online-learning/
>>> [3] http://sf.flink-forward.org/kb_sessions/introducing-flink-te
>>> nsorflow/
>>> [4] http://sf.flink-forward.org/kb_sessions/non-flink-machine-le
>>> arning-on-flink/
>>> [5] http://sf.flink-forward.org/kb_sessions/streaming-deep-learn
>>> ing-scenarios-with-flink/
>>>
>>> Cheers,
>>> Gabor
>>>
>>>
>>> On 2017-02-23 15:19, Katherin Eri wrote:
>>>
>>>> I have asked already some teams for useful cases, but all of them need
>>>> time
>>>> to think.
>>>> During analysis something will finally arise.
>>>> May be we can ask partners of Flink  for cases? Data Artisans got
>>>> results
>>>> of customers survey: [1], ML better support is wanted, so we could ask
>>>> what
>>>> exactly is necessary.
>>>>
>>>> [1] http://data-artisans.com/flink-user-survey-2016-part-2/
>>>>
>>>> 23 февр. 2017 г. 4:32 PM пользователь "Stavros Kontopoulos"
<
>>>> st.kontopoulos@gmail.com> написал:
>>>>
>>>> +100 for a design doc.
>>>>>
>>>>> Could we also set a roadmap after some time-boxed investigation
>>>>> captured in
>>>>> that document? We need action.
>>>>>
>>>>> Looking forward to work on this (whatever that might be) ;) Also are
>>>>> there
>>>>> any data supporting one direction or the other from a customer
>>>>> perspective?
>>>>> It would help to make more informed decisions.
>>>>>
>>>>> On Thu, Feb 23, 2017 at 2:23 PM, Katherin Eri <katherinmail@gmail.com>
>>>>> wrote:
>>>>>
>>>>> Yes, ok.
>>>>>> let's start some design document, and write down there already
>>>>>> mentioned
>>>>>> ideas about: parameter server, about clipper and others. Would be
>>>>>> nice if
>>>>>> we will also map this approaches to cases.
>>>>>> Will work on it collaboratively on each topic, may be finally we
will
>>>>>>
>>>>> form
>>>>>
>>>>>> some picture, that could be agreed with committers.
>>>>>> @Gabor, could you please start such shared doc, as you have already
>>>>>>
>>>>> several
>>>>>
>>>>>> ideas proposed?
>>>>>>
>>>>>> чт, 23 февр. 2017, 15:06 Gábor Hermann <mail@gaborhermann.com>:
>>>>>>
>>>>>> I agree, that it's better to go in one direction first, but I think
>>>>>>> online and offline with streaming API can go somewhat parallel
later.
>>>>>>>
>>>>>> We
>>>>>
>>>>>> could set a short-term goal, concentrate initially on one direction,
>>>>>>>
>>>>>> and
>>>>>
>>>>>> showcase that direction (e.g. in a blogpost). But first, we should
>>>>>>> list
>>>>>>> the pros/cons in a design doc as a minimum. Then make a decision
what
>>>>>>> direction to go. Would that be feasible?
>>>>>>>
>>>>>>> On 2017-02-23 12:34, Katherin Eri wrote:
>>>>>>>
>>>>>>> I'm not sure that this is feasible, doing all at the same time
could
>>>>>>>>
>>>>>>> mean
>>>>>>
>>>>>>> doing nothing((((
>>>>>>>> I'm just afraid, that words: we will work on streaming not
on
>>>>>>>>
>>>>>>> batching,
>>>>>
>>>>>> we
>>>>>>>
>>>>>>>> have no commiter's time for this, mean that yes, we started
work on
>>>>>>>> FLINK-1730, but nobody will commit this work in the end,
as it
>>>>>>>>
>>>>>>> already
>>>>>
>>>>>> was
>>>>>>>
>>>>>>>> with this ticket.
>>>>>>>>
>>>>>>>> 23 февр. 2017 г. 14:26 пользователь "Gábor
Hermann" <
>>>>>>>>
>>>>>>> mail@gaborhermann.com>
>>>>>>>
>>>>>>>> написал:
>>>>>>>>
>>>>>>>> @Theodore: Great to hear you think the "batch on streaming"
approach
>>>>>>>>>
>>>>>>>> is
>>>>>>
>>>>>>> possible! Of course, we need to pay attention all the pitfalls
>>>>>>>>>
>>>>>>>> there,
>>>>>
>>>>>> if we
>>>>>>>
>>>>>>>> go that way.
>>>>>>>>>
>>>>>>>>> +1 for a design doc!
>>>>>>>>>
>>>>>>>>> I would add that it's possible to make efforts in all
the three
>>>>>>>>>
>>>>>>>> directions
>>>>>>>
>>>>>>>> (i.e. batch, online, batch on streaming) at the same time.
Although,
>>>>>>>>>
>>>>>>>> it
>>>>>>
>>>>>>> might be worth to concentrate on one. E.g. it would not be so
useful
>>>>>>>>>
>>>>>>>> to
>>>>>>
>>>>>>> have the same batch algorithms with both the batch API and streaming
>>>>>>>>>
>>>>>>>> API.
>>>>>>>
>>>>>>>> We can decide later.
>>>>>>>>>
>>>>>>>>> The design doc could be partitioned to these 3 directions,
and we
>>>>>>>>>
>>>>>>>> can
>>>>>
>>>>>> collect there the pros/cons too. What do you think?
>>>>>>>>>
>>>>>>>>> Cheers,
>>>>>>>>> Gabor
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On 2017-02-23 12:13, Theodore Vasiloudis wrote:
>>>>>>>>>
>>>>>>>>> Hello all,
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> @Gabor, we have discussed the idea of using the streaming
API to
>>>>>>>>>>
>>>>>>>>> write
>>>>>>
>>>>>>> all
>>>>>>>
>>>>>>>> of our ML algorithms with a couple of people offline,
>>>>>>>>>> and I think it might be possible and is generally
worth a shot.
>>>>>>>>>> The
>>>>>>>>>> approach we would take would be close to Vowpal Wabbit,
not
>>>>>>>>>> exactly
>>>>>>>>>> "online", but rather "fast-batch".
>>>>>>>>>>
>>>>>>>>>> There will be problems popping up again, even for
very simple
>>>>>>>>>> algos
>>>>>>>>>>
>>>>>>>>> like
>>>>>>>
>>>>>>>> on
>>>>>>>>>> line linear regression with SGD [1], but hopefully
fixing those
>>>>>>>>>>
>>>>>>>>> will
>>>>>
>>>>>> be
>>>>>>
>>>>>>> more aligned with the priorities of the community.
>>>>>>>>>>
>>>>>>>>>> @Katherin, my understanding is that given the limited
resources,
>>>>>>>>>>
>>>>>>>>> there
>>>>>>
>>>>>>> is
>>>>>>>
>>>>>>>> no development effort focused on batch processing right now.
>>>>>>>>>>
>>>>>>>>>> So to summarize, it seems like there are people willing
to work on
>>>>>>>>>>
>>>>>>>>> ML
>>>>>
>>>>>> on
>>>>>>>
>>>>>>>> Flink, but nobody is sure how to do it.
>>>>>>>>>> There are many directions we could take (batch, online,
batch on
>>>>>>>>>> streaming), each with its own merits and downsides.
>>>>>>>>>>
>>>>>>>>>> If you want we can start a design doc and move the
conversation
>>>>>>>>>>
>>>>>>>>> there,
>>>>>>
>>>>>>> come
>>>>>>>>>> up with a roadmap and start implementing.
>>>>>>>>>>
>>>>>>>>>> Regards,
>>>>>>>>>> Theodore
>>>>>>>>>>
>>>>>>>>>> [1]
>>>>>>>>>> http://apache-flink-user-mailing-list-archive.2336050.n4.
>>>>>>>>>> nabble.com/Understanding-connected-streams-use-without-times
>>>>>>>>>> tamps-td10241.html
>>>>>>>>>>
>>>>>>>>>> On Tue, Feb 21, 2017 at 11:17 PM, Gábor Hermann
<
>>>>>>>>>>
>>>>>>>>> mail@gaborhermann.com
>>>>>>
>>>>>>> wrote:
>>>>>>>>>>
>>>>>>>>>> It's great to see so much activity in this discussion
:)
>>>>>>>>>>
>>>>>>>>>>> I'll try to add my thoughts.
>>>>>>>>>>>
>>>>>>>>>>> I think building a developer community (Till's
2. point) can be
>>>>>>>>>>>
>>>>>>>>>> slightly
>>>>>>>
>>>>>>>> separated from what features we should aim for (1. point)
and
>>>>>>>>>>>
>>>>>>>>>> showcasing
>>>>>>>
>>>>>>>> (3. point). Thanks Till for bringing up the ideas for
>>>>>>>>>>>
>>>>>>>>>> restructuring,
>>>>>
>>>>>> I'm
>>>>>>>
>>>>>>>> sure we'll find a way to make the development process more
>>>>>>>>>>>
>>>>>>>>>> dynamic.
>>>>>
>>>>>> I'll
>>>>>>>
>>>>>>>> try to address the rest here.
>>>>>>>>>>>
>>>>>>>>>>> It's hard to choose directions between streaming
and batch ML. As
>>>>>>>>>>>
>>>>>>>>>> Theo
>>>>>>
>>>>>>> has
>>>>>>>>>>> indicated, not much online ML is used in production,
but Flink
>>>>>>>>>>> concentrates
>>>>>>>>>>> on streaming, so online ML would be a better
fit for Flink.
>>>>>>>>>>>
>>>>>>>>>> However,
>>>>>
>>>>>> as
>>>>>>>
>>>>>>>> most of you argued, there's definite need for batch ML. But
batch
>>>>>>>>>>>
>>>>>>>>>> ML
>>>>>
>>>>>> seems
>>>>>>>>>>> hard to achieve because there are blocking issues
with
>>>>>>>>>>> persisting,
>>>>>>>>>>> iteration paths etc. So it's no good either way.
>>>>>>>>>>>
>>>>>>>>>>> I propose a seemingly crazy solution: what if
we developed batch
>>>>>>>>>>> algorithms also with the streaming API? The batch
API would
>>>>>>>>>>>
>>>>>>>>>> clearly
>>>>>
>>>>>> seem
>>>>>>>
>>>>>>>> more suitable for ML algorithms, but there a lot of benefits
of
>>>>>>>>>>>
>>>>>>>>>> this
>>>>>
>>>>>> approach too, so it's clearly worth considering. Flink also has
>>>>>>>>>>>
>>>>>>>>>> the
>>>>>
>>>>>> high
>>>>>>>
>>>>>>>> level vision of "streaming for everything" that would clearly
fit
>>>>>>>>>>>
>>>>>>>>>> this
>>>>>>
>>>>>>> case. What do you all think about this? Do you think this solution
>>>>>>>>>>>
>>>>>>>>>> would
>>>>>>>
>>>>>>>> be
>>>>>>>>>>> feasible? I would be happy to make a more elaborate
proposal, but
>>>>>>>>>>>
>>>>>>>>>> I
>>>>>
>>>>>> push
>>>>>>>
>>>>>>>> my
>>>>>>>>>>> main ideas here:
>>>>>>>>>>>
>>>>>>>>>>> 1) Simplifying by using one system
>>>>>>>>>>> It could simplify the work of both the users
and the developers.
>>>>>>>>>>>
>>>>>>>>>> One
>>>>>
>>>>>> could
>>>>>>>>>>> execute training once, or could execute it periodically
e.g. by
>>>>>>>>>>>
>>>>>>>>>> using
>>>>>>
>>>>>>> windows. Low-latency serving and training could be done in the
>>>>>>>>>>>
>>>>>>>>>> same
>>>>>
>>>>>> system.
>>>>>>>>>>> We could implement incremental algorithms, without
any side
>>>>>>>>>>> inputs
>>>>>>>>>>>
>>>>>>>>>> for
>>>>>>
>>>>>>> combining online learning (or predictions) with batch learning.
Of
>>>>>>>>>>> course,
>>>>>>>>>>> all the logic describing these must be somehow
implemented (e.g.
>>>>>>>>>>> synchronizing predictions with training), but
it should be easier
>>>>>>>>>>>
>>>>>>>>>> to
>>>>>
>>>>>> do
>>>>>>>
>>>>>>>> so
>>>>>>>>>>> in one system, than by combining e.g. the batch
and streaming
>>>>>>>>>>> API.
>>>>>>>>>>>
>>>>>>>>>>> 2) Batch ML with the streaming API is not harder
>>>>>>>>>>> Despite these benefits, it could seem harder
to implement batch
>>>>>>>>>>> ML
>>>>>>>>>>>
>>>>>>>>>> with
>>>>>>>
>>>>>>>> the streaming API, but in my opinion it's not. There are
more
>>>>>>>>>>>
>>>>>>>>>> flexible,
>>>>>>>
>>>>>>>> lower-level optimization potentials with the streaming API.
Most
>>>>>>>>>>> distributed ML algorithms use a lower-level model
than the batch
>>>>>>>>>>>
>>>>>>>>>> API
>>>>>
>>>>>> anyway, so sometimes it feels like forcing the algorithm logic
>>>>>>>>>>>
>>>>>>>>>> into
>>>>>
>>>>>> the
>>>>>>>
>>>>>>>> training API and tweaking it. Although we could not use the
batch
>>>>>>>>>>> primitives like join, we would have the E.g.
in my experience
>>>>>>>>>>> with
>>>>>>>>>>> implementing a distributed matrix factorization
algorithm [1], I
>>>>>>>>>>>
>>>>>>>>>> couldn't
>>>>>>>
>>>>>>>> do a simple optimization because of the limitations of the
>>>>>>>>>>>
>>>>>>>>>> iteration
>>>>>
>>>>>> API
>>>>>>>
>>>>>>>> [2]. Even if we pushed all the development effort to make
the
>>>>>>>>>>>
>>>>>>>>>> batch
>>>>>
>>>>>> API
>>>>>>>
>>>>>>>> more suitable for ML there would be things we couldn't do.
E.g.
>>>>>>>>>>>
>>>>>>>>>> there
>>>>>>
>>>>>>> are
>>>>>>>
>>>>>>>> approaches for updating a model iteratively without locks
[3,4]
>>>>>>>>>>>
>>>>>>>>>> (i.e.
>>>>>>
>>>>>>> somewhat asynchronously), and I don't see a clear way to implement
>>>>>>>>>>>
>>>>>>>>>> such
>>>>>>>
>>>>>>>> algorithms with the batch API.
>>>>>>>>>>>
>>>>>>>>>>> 3) Streaming community (users and devs) benefit
>>>>>>>>>>> The Flink streaming community in general would
also benefit from
>>>>>>>>>>>
>>>>>>>>>> this
>>>>>>
>>>>>>> direction. There are many features needed in the streaming API
for
>>>>>>>>>>>
>>>>>>>>>> ML
>>>>>>
>>>>>>> to
>>>>>>>
>>>>>>>> work, but this is also true for the batch API. One really
>>>>>>>>>>>
>>>>>>>>>> important
>>>>>
>>>>>> is
>>>>>>
>>>>>>> the
>>>>>>>>>>> loops API (a.k.a. iterative DataStreams) [5].
There has been a
>>>>>>>>>>> lot
>>>>>>>>>>>
>>>>>>>>>> of
>>>>>>
>>>>>>> effort (mostly from Paris) for making it mature enough [6]. Kate
>>>>>>>>>>> mentioned
>>>>>>>>>>> using GPUs, and I'm sure they have uses in streaming
generally
>>>>>>>>>>>
>>>>>>>>>> [7].
>>>>>
>>>>>> Thus,
>>>>>>>
>>>>>>>> by improving the streaming API to allow ML algorithms, the
>>>>>>>>>>>
>>>>>>>>>> streaming
>>>>>
>>>>>> API
>>>>>>>
>>>>>>>> benefit too (which is important as they have a lot more production
>>>>>>>>>>>
>>>>>>>>>> users
>>>>>>>
>>>>>>>> than the batch API).
>>>>>>>>>>>
>>>>>>>>>>> 4) Performance can be at least as good
>>>>>>>>>>> I believe the same performance could be achieved
with the
>>>>>>>>>>>
>>>>>>>>>> streaming
>>>>>
>>>>>> API
>>>>>>>
>>>>>>>> as
>>>>>>>>>>> with the batch API. Streaming API is much closer
to the runtime
>>>>>>>>>>>
>>>>>>>>>> than
>>>>>
>>>>>> the
>>>>>>>
>>>>>>>> batch API. For corner-cases, with runtime-layer optimizations
of
>>>>>>>>>>>
>>>>>>>>>> batch
>>>>>>
>>>>>>> API,
>>>>>>>>>>> we could find a way to do the same (or similar)
optimization for
>>>>>>>>>>>
>>>>>>>>>> the
>>>>>
>>>>>> streaming API (see my previous point). Such case could be using
>>>>>>>>>>>
>>>>>>>>>> managed
>>>>>>>
>>>>>>>> memory (and spilling to disk). There are also benefits by
default,
>>>>>>>>>>>
>>>>>>>>>> e.g.
>>>>>>>
>>>>>>>> we
>>>>>>>>>>> would have a finer grained fault tolerance with
the streaming
>>>>>>>>>>> API.
>>>>>>>>>>>
>>>>>>>>>>> 5) We could keep batch ML API
>>>>>>>>>>> For the shorter term, we should not throw away
all the algorithms
>>>>>>>>>>> implemented with the batch API. By pushing forward
the
>>>>>>>>>>> development
>>>>>>>>>>>
>>>>>>>>>> with
>>>>>>>
>>>>>>>> side inputs we could make them usable with streaming API.
Then, if
>>>>>>>>>>>
>>>>>>>>>> the
>>>>>>
>>>>>>> library gains some popularity, we could replace the algorithms
in
>>>>>>>>>>>
>>>>>>>>>> the
>>>>>>
>>>>>>> batch
>>>>>>>>>>> API with streaming ones, to avoid the performance
costs of e.g.
>>>>>>>>>>>
>>>>>>>>>> not
>>>>>
>>>>>> being
>>>>>>>
>>>>>>>> able to persist.
>>>>>>>>>>>
>>>>>>>>>>> 6) General tools for implementing ML algorithms
>>>>>>>>>>> Besides implementing algorithms one by one, we
could give more
>>>>>>>>>>>
>>>>>>>>>> general
>>>>>>
>>>>>>> tools for making it easier to implement algorithms. E.g. parameter
>>>>>>>>>>>
>>>>>>>>>> server
>>>>>>>
>>>>>>>> [8,9]. Theo also mentioned in another thread that TensorFlow
has a
>>>>>>>>>>> similar
>>>>>>>>>>> model to Flink streaming, we could look into
that too. I think
>>>>>>>>>>>
>>>>>>>>>> often
>>>>>
>>>>>> when
>>>>>>>
>>>>>>>> deploying a production ML system, much more configuration
and
>>>>>>>>>>>
>>>>>>>>>> tweaking
>>>>>>
>>>>>>> should be done than e.g. Spark MLlib allows. Why not allow that?
>>>>>>>>>>>
>>>>>>>>>>> 7) Showcasing
>>>>>>>>>>> Showcasing this could be easier. We could say
that we're doing
>>>>>>>>>>>
>>>>>>>>>> batch
>>>>>
>>>>>> ML
>>>>>>>
>>>>>>>> with a streaming API. That's interesting in its own. IMHO
this
>>>>>>>>>>> integration
>>>>>>>>>>> is also a more approachable way towards end-to-end
ML.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> Thanks for reading so far :)
>>>>>>>>>>>
>>>>>>>>>>> [1] https://github.com/apache/flink/pull/2819
>>>>>>>>>>> [2] https://issues.apache.org/jira/browse/FLINK-2396
>>>>>>>>>>> [3] https://people.eecs.berkeley.edu/~brecht/papers/hogwildTR.pd
>>>>>>>>>>> f
>>>>>>>>>>> [4] https://www.usenix.org/system/files/conference/hotos13/hotos
>>>>>>>>>>> 13-final77.pdf
>>>>>>>>>>> [5] https://cwiki.apache.org/confluence/display/FLINK/FLIP-15+
>>>>>>>>>>> Scoped+Loops+and+Job+Termination
>>>>>>>>>>> [6] https://github.com/apache/flink/pull/1668
>>>>>>>>>>> [7] http://lsds.doc.ic.ac.uk/sites/default/files/saber-sigmod16.
>>>>>>>>>>>
>>>>>>>>>> pdf
>>>>>
>>>>>> [8] https://www.cs.cmu.edu/~muli/file/parameter_server_osdi14.pdf
>>>>>>>>>>> [9] http://apache-flink-mailing-list-archive.1008284.n3.nabble.
>>>>>>>>>>> com/Using-QueryableState-inside-Flink-jobs-and-
>>>>>>>>>>> Parameter-Server-implementation-td15880.html
>>>>>>>>>>>
>>>>>>>>>>> Cheers,
>>>>>>>>>>> Gabor
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> --
>>>>>>>
>>>>>> *Yours faithfully, *
>>>>>>
>>>>>> *Kate Eri.*
>>>>>>
>>>>>>
>>
>


-- 
Roberto Bentivoglio
CTO
e. roberto.bentivoglio@radicalbit.io
Radicalbit S.r.l.
radicalbit.io

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