flink-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Katherin Eri <katherinm...@gmail.com>
Subject Re: Machine Learning on Flink - Next steps
Date Fri, 03 Mar 2017 15:41:38 GMT
Thank you, Theodore.

Shortly speaking I vote for:
1) Online learning
2) Low-latency prediction serving -> Offline learning with the batch API

In details:
1) If streaming is strong side of Flink lets use it, and try to support
some online learning or light weight inmemory learning algorithms. Try to
build pipeline for them.

2) I think that Flink should be part of production ecosystem, and if now
productions require ML support, multiple models deployment and so on, we
should serve this. But in my opinion we shouldn’t compete with such
projects like PredictionIO, but serve them, to be an execution core. But
that means a lot:

a. Offline training should be supported, because typically most of ML algs
are for offline training.
b. Model lifecycle should be supported:
ETL+transformation+training+scoring+exploitation quality monitoring

I understand that batch world is full of competitors, but for me that
doesn’t mean that batch should be ignored. I think that separated
streaming/batching applications causes additional deployment and
exploitation overhead which typically tried to be avoided. That means that
we should attract community to this problem in my opinion.


пт, 3 мар. 2017 г. в 15:34, Theodore Vasiloudis <
theodoros.vasiloudis@gmail.com>:

Hello all,

>From our previous discussion started by Stavros, we decided to start a
planning document [1]
to figure out possible next steps for ML on Flink.

Our concerns where mainly ensuring active development while satisfying the
needs of
the community.

We have listed a number of proposals for future work in the document. In
short they are:

   - Offline learning with the batch API
   - Online learning
   - Offline learning with the streaming API
   - Low-latency prediction serving

I saw there is a number of people willing to work on ML for Flink, but the
truth is that we cannot
cover all of these suggestions without fragmenting the development too much.

So my recommendation is to pick out 2 of these options, create design
documents and build prototypes for each library.
We can then assess their viability and together with the community decide
if we should try
to include one (or both) of them in the main Flink distribution.

So I invite people to express their opinion about which task they would be
willing to contribute
and hopefully we can settle on two of these options.

Once that is done we can decide how we do the actual work. Since this is
highly experimental
I would suggest we work on repositories where we have complete control.

For that purpose I have created an organization [2] on Github which we can
use to create repositories and teams that work on them in an organized
manner.
Once enough work has accumulated we can start discussing contributing the
code
to the main distribution.

Regards,
Theodore

[1]
https://docs.google.com/document/d/1afQbvZBTV15qF3vobVWUjxQc49h3Ud06MIRhahtJ6dw/
[2] https://github.com/flinkml

-- 

*Yours faithfully, *

*Kate Eri.*

Mime
  • Unnamed multipart/alternative (inline, None, 0 bytes)
View raw message