metron-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Andrew Psaltis <psaltis.and...@gmail.com>
Subject Re: Metron-265 Model as a Service
Date Thu, 07 Jul 2016 18:14:59 GMT
Thanks Casey, that helps.

RE: I am talking about model execution here.  The endpoints are distributed
across the cluster and the storm bolt chooses a service to use (with a bias
toward using one that is local to that bolt) and the request is made to the
endpoint, which scores the input and returns the response.

This makes sense. Depending on volume and velocity of data seems like this
could get expensive.,


RE: Model service, if that term means what I think it means, is almost
entirely done inside of zookeeper.  For clarity, I'm talking about service
discovery (bolt discovers which endpoints serve which models) and model
updates

Thanks this helps to clarify it quite a bit.  Considering both the storm
bolts and the model service will be deployed on Yarn, could the bolts not
use the Yarn registry to identify which model service to connect to before
making a request?

How do you scale the model service endpoints if they have a preference for
which model they serve? And each is a simple REST (or another more
performant protocol) service as the document describes?



Thanks,
Andrew

On Thu, Jul 7, 2016 at 1:51 PM, Casey Stella <cestella@gmail.com> wrote:

> Great questions Andrew.  Thanks for the interest. :)
>
> RE:: "which is why there would be a caching layer set in front of it at the
> Storm bolt level"
>
> Right now we have a LRU caching layer in front of the HBase enrichment
> adapters, so it would work similarly.  You can imagine, the range of inputs
> is likely not perfectly random, so it's reasonable for the cache to have a
> non-empty working set.  Take for instance a DGA model; the input would be a
> domain and most organizations will have an uneven distribution of domains
> they access with a heavy skew toward a small number.
>
> RE: In this scenario, you can at least scale out via load balancing (i.e.
> multiple model services serving the same model) since the models are
> immutable.
>
> I am talking about model execution here.  The endpoints are distributed
> across the cluster and the storm bolt chooses a service to use (with a bias
> toward using one that is local to that bolt) and the request is made to the
> endpoint, which scores the input and returns the response.
>
> Model service, if that term means what I think it means, is almost entirely
> done inside of zookeeper.  For clarity, I'm talking about service discovery
> (bolt discovers which endpoints serve which models) and model updates.  We
> are not sending the model around to any bolts or any such thing, just for
> clarity sake.
>
>
>
> On Thu, Jul 7, 2016 at 9:47 AM, Andrew Psaltis <psaltis.andrew@gmail.com>
> wrote:
>
> > Thanks Casey! Couple of quick questions.
> >
> > RE:: "which is why there would be a caching layer set in front of it at
> the
> > Storm bolt level"
> > Hmm, would this be of the results of model execution? Would this really
> > work when each tuple may contain totally different data? Or is the
> caching
> > going to be smart enough that it will look at all the data passed in and
> > determine that an identical tuple has already been evaluated so serve the
> > result out of cache?
> >
> > RE: "Also, we would prefer local instances of the service when and where
> > possible"
> > Perfect makes sense.
> >
> > RE: Serving many models from every storm bolt is also fairly expensive.
> > I can see how it could be, but couldn't  we can make sure that not all
> > models live in every bolt?
> >
> > RE: In this scenario, you can at least scale out via load balancing (i.e.
> > multiple model services serving the same model) since the models are
> > immutable.
> > This seems to address the model serving, not model execution service.
> > Having yet one more layer to scale and mange also seems like it
> > would further complicate things. Could we not just also scale the bolts?
> >
> > Thanks,
> > Andrew
> >
> >
> >
> >
> > On Thu, Jul 7, 2016 at 12:37 PM, Casey Stella <cestella@gmail.com>
> wrote:
> >
> > > So, regarding the expense of communication; I tend to agree that it is
> > > expensive, which is why there would be a caching layer set in front of
> it
> > > at the Storm bolt level.  Also, we would prefer local instances of the
> > > service when and where possible.  Serving many models from every storm
> > bolt
> > > is also fairly expensive.  In this scenario, you can at least scale out
> > via
> > > load balancing (i.e. multiple model services serving the same model)
> > since
> > > the models are immutable.
> > >
> > > On Thu, Jul 7, 2016 at 9:24 AM, Andrew Psaltis <
> psaltis.andrew@gmail.com
> > >
> > > wrote:
> > >
> > > > OK that makes sense. So the doc attached to this JIRA[1] just speaks
> to
> > > the
> > > > Model serving. Is there a doc for the model service? And by making
> > this a
> > > > separate service we are saying that for every
> “MODEL_APPLY(model_name,
> > > > param_1, param_2, …, param_n)” we are potentially going to go across
> > the
> > > > wire and have a model executed? That seems pretty expensive, no?
> > > >
> > > > Thanks,
> > > > Andrew
> > > >
> > > > [1] https://issues.apache.org/jira/browse/METRON-265
> > > >
> > > > On Thu, Jul 7, 2016 at 12:20 PM, Casey Stella <cestella@gmail.com>
> > > wrote:
> > > >
> > > > > The "REST" model service, which I place in quotes because there is
> > some
> > > > > strong discussion about whether REST is a reasonable transport for
> > > this,
> > > > is
> > > > > responsible for providing the model.  The scoring/model application
> > > > happens
> > > > > in the model service and the results get transferred back to the
> > storm
> > > > bolt
> > > > > that calls it.
> > > > >
> > > > > Casey
> > > > >
> > > > > On Thu, Jul 7, 2016 at 9:17 AM, Andrew Psaltis <
> > > psaltis.andrew@gmail.com
> > > > >
> > > > > wrote:
> > > > >
> > > > > > Trying to make sure I grok this thread and the word doc attached
> to
> > > the
> > > > > > JIRA. The word doc and JIRA speak to a Model Service Service
and
> > that
> > > > the
> > > > > > REST service will be responsible for serving up models. However,
> > part
> > > > of
> > > > > > this conversation seems to suggest that the model execution
will
> > > > actually
> > > > > > occur at the REST service .. in particular this comment from
> James:
> > > > > >
> > > > > > "There are several reasons to decouple model execution from
> Storm:"
> > > > > >
> > > > > > If the model execution is decoupled from Storm then it appears
> that
> > > the
> > > > > > REST service will be executing the model, not just serving it
up,
> > is
> > > > that
> > > > > > correct?
> > > > > >
> > > > > > Thanks,
> > > > > > Andrew
> > > > > >
> > > > > >
> > > > > >
> > > > > > On Thu, Jul 7, 2016 at 11:51 AM, Casey Stella <
> cestella@gmail.com>
> > > > > wrote:
> > > > > >
> > > > > > > Regarding the performance of REST:
> > > > > > >
> > > > > > > Yep, so everyone seems to be worried about the performance
> > > > implications
> > > > > > for
> > > > > > > REST.  I made this comment on the JIRA, but I'll repeat
it here
> > for
> > > > > > broader
> > > > > > > discussion:
> > > > > > >
> > > > > > > My choice of REST was mostly due to the fact that I want
to
> > support
> > > > > > > > multi-language (I think that's a very important requirement)
> > and
> > > > > there
> > > > > > > are
> > > > > > > > REST libraries for pretty much everything. I do agree,
> however,
> > > > that
> > > > > > JSON
> > > > > > > > transport can get chunky. How about a compromise and
use
> REST,
> > > but
> > > > > the
> > > > > > > > input and output payloads for scoring are Maps encoded
in
> > msgpack
> > > > > > rather
> > > > > > > > than JSON. There is a msgpack library for pretty much
every
> > > > language
> > > > > > out
> > > > > > > > there (almost) and certainly all of the ones we'd
like to
> > target.
> > > > > > > >
> > > > > > >
> > > > > > >
> > > > > > > > The other option is to just create and expose protobuf
> bindings
> > > > > (thrift
> > > > > > > > doesn't have a native client for R) for all of the
languages
> > that
> > > > we
> > > > > > want
> > > > > > > > to support. I'm perfectly fine with that, but I had
some
> > worries
> > > > > about
> > > > > > > the
> > > > > > > > maturity of the bindings.
> > > > > > > >
> > > > > > >
> > > > > > >
> > > > > > > > The final option, as you suggest, is to just use raw
> sockets. I
> > > > think
> > > > > > if
> > > > > > > > we went that route, we might have to create a layer
for each
> > > > language
> > > > > > > > rather than relying on model creators to create a
TCP
> server. I
> > > > > thought
> > > > > > > > that might be a bit onerous for a MVP.
> > > > > > > >
> > > > > > >
> > > > > > >
> > > > > > > > Given the discussion, though, what it has made me
aware of is
> > > that
> > > > we
> > > > > > > > might not want to dictate a transport mechanism at
all, but
> > > rather
> > > > > > allow
> > > > > > > > that to be pluggable and extensible (so each model
would be
> > > > > associated
> > > > > > > with
> > > > > > > > a transport mechanism handler that would know how
to
> > communicate
> > > to
> > > > > it.
> > > > > > > We
> > > > > > > > would provide default mechanisms for msgpack over
REST, JSON
> > over
> > > > > REST
> > > > > > > and
> > > > > > > > maybe msgpack over raw TCP.) Thoughts?
> > > > > > >
> > > > > > >
> > > > > > > Regarding PMML:
> > > > > > >
> > > > > > > I tend to agree with James that PMML is too restrictive
as to
> > > models
> > > > it
> > > > > > can
> > > > > > > represent and I have not had great experiences with it
in
> > > production.
> > > > > > > Also, the open source libraries for PMML have licensing
issues
> > > (jpmml
> > > > > > > requires an older version to accommodate our licensing
> > > requirements).
> > > > > > >
> > > > > > > Regarding workflow:
> > > > > > >
> > > > > > > At the moment, I'd like to focus on getting a generalized
> > > > > infrastructure
> > > > > > > for model scoring and updating put in place.   This means,
this
> > > > > > > architecture takes up the baton from the point when a model
is
> > > > > > > trained/created.  Also, I have attempted to be generic
in terms
> > of
> > > > > output
> > > > > > > of the model (a map of results) so it can fit any type
of model
> > > that
> > > > I
> > > > > > can
> > > > > > > think of.  If that's not the case, let me know, though.
> > > > > > >
> > > > > > > For instance, for clustering, you would probably emit the
> cluster
> > > id
> > > > > > > associated with the input and that would be added to the
> message
> > as
> > > > it
> > > > > > > passes through the storm topology.  The model is responsible
> for
> > > > > > processing
> > > > > > > the input and constructing properly formed output.
> > > > > > >
> > > > > > > Casey
> > > > > > >
> > > > > > >
> > > > > > > On Tue, Jul 5, 2016 at 3:45 PM, Debo Dutta (dedutta) <
> > > > > dedutta@cisco.com>
> > > > > > > wrote:
> > > > > > >
> > > > > > > > Following up on the thread a little late …. Awesome
start
> > Casey.
> > > > Some
> > > > > > > > comments:
> > > > > > > > * Model execution
> > > > > > > > ** I am guessing the model execution will be on YARN
only for
> > > now.
> > > > > This
> > > > > > > is
> > > > > > > > fine, but the REST call could have an overhead - depends
on
> the
> > > > > speed.
> > > > > > > > * PMML: won’t we have to choose some DSL for describing
> models?
> > > > > > > > * Model:
> > > > > > > > ** workflow vs a model -  do we care about the “workflow"
> that
> > > > leads
> > > > > to
> > > > > > > > the models or just the “model"? For example, we
might start
> > with
> > > n
> > > > > > > features
> > > > > > > > —> do feature selection to choose k (or apply
a transform
> > > function)
> > > > > —>
> > > > > > > > apply a model etc
> > > > > > > > * Use cases - I can see this working for n-ary classification
> > > style
> > > > > > > models
> > > > > > > > easily. Will the same mechanism be used for stuff
like
> > clustering
> > > > (or
> > > > > > > > intermediate steps like feature selection alone).
> > > > > > > >
> > > > > > > > Thx
> > > > > > > > debo
> > > > > > > >
> > > > > > > >
> > > > > > > >
> > > > > > > >
> > > > > > > > On 7/5/16, 3:24 PM, "James Sirota" <jsirota@apache.org>
> wrote:
> > > > > > > >
> > > > > > > > >Simon,
> > > > > > > > >
> > > > > > > > >There are several reasons to decouple model execution
from
> > > Storm:
> > > > > > > > >
> > > > > > > > >- Reliability: It's much easier to handle a failed
service
> > than
> > > a
> > > > > > failed
> > > > > > > > bolt.  You can also troubleshoot without having to
bring down
> > the
> > > > > > > topology
> > > > > > > > >- Complexity: you de-couple the model logic from
Storm logic
> > and
> > > > can
> > > > > > > > manage it independently of Storm
> > > > > > > > >- Portability: you can swap the model guts (switch
from
> Spark
> > to
> > > > > > Flink,
> > > > > > > > etc) and as long as you maintain the interface you
are good
> to
> > go
> > > > > > > > >- Consistency: since we want to expose our models
the same
> way
> > > we
> > > > > > expose
> > > > > > > > threat intel then it makes sense to expose them as
a service
> > > > > > > > >
> > > > > > > > >In our vision for Metron we want to make it easy
to uptake
> and
> > > > share
> > > > > > > > models.  I think well-defined interfaces and programmatic
> ways
> > of
> > > > > > > > deployment, lifecycle management, and scoring via
> well-defined
> > > REST
> > > > > > > > interfaces will make this task easier.  We can do
a few
> things
> > to
> > > > > > > > >
> > > > > > > > >With respect to PMML I personally had not had
much luck with
> > it
> > > in
> > > > > > > > production.  I would prefer models as POJOs.
> > > > > > > > >
> > > > > > > > >Thanks,
> > > > > > > > >James
> > > > > > > > >
> > > > > > > > >04.07.2016, 16:07, "Simon Ball" <sball@hortonworks.com>:
> > > > > > > > >> Since the models' parameters and execution
algorithm are
> > > likely
> > > > to
> > > > > > be
> > > > > > > > small, why not have the model store push the model
changes
> and
> > > > > scoring
> > > > > > > > direct to the bolts and execute within storm. This
negates
> the
> > > > > overhead
> > > > > > > of
> > > > > > > > a rest call to the model server, and the need for
discovery
> of
> > > the
> > > > > > model
> > > > > > > > server in zookeeper.
> > > > > > > > >>
> > > > > > > > >> Something like the way ranger policies are
updated /
> cached
> > in
> > > > > > plugins
> > > > > > > > would seem to make sense, so that we're distributing
the
> model
> > > > > > execution
> > > > > > > > directly into the enrichment pipeline rather than
collecting
> > in a
> > > > > > central
> > > > > > > > service.
> > > > > > > > >>
> > > > > > > > >> This would work with simple models on single
events, but
> may
> > > > > > struggle
> > > > > > > > with correlation based models. However, those could
be
> handled
> > in
> > > > > storm
> > > > > > > by
> > > > > > > > pushing into a windowing trident topology or something
of the
> > > sort,
> > > > > or
> > > > > > > even
> > > > > > > > with a parallel spark streaming job using the same
method of
> > > > > > distributing
> > > > > > > > models.
> > > > > > > > >>
> > > > > > > > >> The real challenge here would be stateful
online models,
> > which
> > > > > seem
> > > > > > > > like a minority case which could be handled by a shared
state
> > > store
> > > > > > such
> > > > > > > as
> > > > > > > > HBase.
> > > > > > > > >>
> > > > > > > > >> You still keep the ability to run different
languages, and
> > > > > > platforms,
> > > > > > > > but wrap managing the parallelism in storm bolts rather
than
> > yarn
> > > > > > > > containers.
> > > > > > > > >>
> > > > > > > > >> We could also consider basing the model protocol
on a a
> > common
> > > > > model
> > > > > > > > language like pmml, thong that is likely to be highly
> limiting.
> > > > > > > > >>
> > > > > > > > >> Simon
> > > > > > > > >>
> > > > > > > > >>>  On 4 Jul 2016, at 22:35, Casey Stella
<
> cestella@gmail.com
> > >
> > > > > wrote:
> > > > > > > > >>>
> > > > > > > > >>>  This is great! I'll capture any requirements
that anyone
> > > wants
> > > > > to
> > > > > > > > >>>  contribute and ensure that the proposed
architecture
> > > > > accommodates
> > > > > > > > them. I
> > > > > > > > >>>  think we should focus on a minimal set
of requirements
> and
> > > an
> > > > > > > > architecture
> > > > > > > > >>>  that does not preclude a larger set.
I have found that
> the
> > > > best
> > > > > > > > driver of
> > > > > > > > >>>  requirements are installed users. :)
> > > > > > > > >>>
> > > > > > > > >>>  For instance, I think a lot of questions
about how often
> > to
> > > > > > update a
> > > > > > > > model
> > > > > > > > >>>  and such should be represented in the
architecture by
> the
> > > > > ability
> > > > > > to
> > > > > > > > >>>  manually update a model, so as long
as we have the
> ability
> > > to
> > > > > > > update,
> > > > > > > > >>>  people can choose when and where to
do it (i.e. time
> based
> > > or
> > > > > some
> > > > > > > > other
> > > > > > > > >>>  trigger). That being said, we don't
want to cause too
> much
> > > > > effort
> > > > > > > for
> > > > > > > > the
> > > > > > > > >>>  user if we can avoid it with features.
> > > > > > > > >>>
> > > > > > > > >>>  In terms of the questions laid out,
here are the
> > constraints
> > > > > from
> > > > > > > the
> > > > > > > > >>>  proposed architecture as I see them.
It'd be great to
> get
> > a
> > > > > sense
> > > > > > of
> > > > > > > > >>>  whether these constraints are too onerous
or where
> they're
> > > not
> > > > > > > > opinionated
> > > > > > > > >>>  enough :
> > > > > > > > >>>
> > > > > > > > >>>    - Model versioning and retention
> > > > > > > > >>>    - We do have the ability to update
models, but the
> > > training
> > > > > and
> > > > > > > > decision
> > > > > > > > >>>       of when to update the model is
left up to the user.
> > We
> > > > may
> > > > > > want
> > > > > > > > to think
> > > > > > > > >>>       deeply about when and where automated
model updates
> > can
> > > > fit
> > > > > > > > >>>       - Also, retention is currently
manual. It might be
> an
> > > > > easier
> > > > > > > win
> > > > > > > > to
> > > > > > > > >>>       set up policies around when to
sunset models (after
> > > newer
> > > > > > > > versions are
> > > > > > > > >>>       added, for instance).
> > > > > > > > >>>    - Model access controls management
> > > > > > > > >>>    - The architecture proposes no constraints
around
> this.
> > As
> > > > it
> > > > > > > stands
> > > > > > > > >>>       now, models are held in HDFS, so
it would inherit
> the
> > > > same
> > > > > > > > security
> > > > > > > > >>>       capabilities from that (user/group
permissions +
> > > Ranger,
> > > > > etc)
> > > > > > > > >>>    - Requirements around concept drift
> > > > > > > > >>>    - I'd love to hear user requirements
around how we
> could
> > > > > > > > automatically
> > > > > > > > >>>       address concept drift. The architecture
as it's
> > > proposed
> > > > > > let's
> > > > > > > > the user
> > > > > > > > >>>       decide when to update models.
> > > > > > > > >>>    - Requirements around model output
> > > > > > > > >>>    - The architecture as it stands just
mandates a JSON
> map
> > > > input
> > > > > > and
> > > > > > > > JSON
> > > > > > > > >>>       map output, so it's up to the model
what they want
> to
> > > > pass
> > > > > > > back.
> > > > > > > > >>>       - It's also up to the model to
document its own
> > output.
> > > > > > > > >>>    - Any model audit and logging requirements
> > > > > > > > >>>    - The architecture proposes no constraints
around
> this.
> > > I'd
> > > > > love
> > > > > > > to
> > > > > > > > see
> > > > > > > > >>>       community guidance around this.
As it stands, we
> just
> > > log
> > > > > > using
> > > > > > > > the same
> > > > > > > > >>>       mechanism as any YARN application.
> > > > > > > > >>>    - What model metrics need to be exposed
> > > > > > > > >>>    - The architecture proposes no constraints
around
> this.
> > > I'd
> > > > > love
> > > > > > > to
> > > > > > > > see
> > > > > > > > >>>       community guidance around this.
> > > > > > > > >>>       - Requirements around failure modes
> > > > > > > > >>>    - We briefly touch on this in the
document, but it is
> > > > probably
> > > > > > not
> > > > > > > > >>>       complete. Service endpoint failure
will result in
> > > > > > blacklisting
> > > > > > > > from a
> > > > > > > > >>>       storm bolt perspective and node
failure should
> result
> > > in
> > > > a
> > > > > > new
> > > > > > > > container
> > > > > > > > >>>       being started by the Yarn application
master.
> Beyond
> > > > that,
> > > > > > the
> > > > > > > > >>>       architecture isn't explicit.
> > > > > > > > >>>
> > > > > > > > >>>>  On Mon, Jul 4, 2016 at 1:49 PM,
James Sirota <
> > > > > jsirota@apache.org
> > > > > > >
> > > > > > > > wrote:
> > > > > > > > >>>>
> > > > > > > > >>>>  I left a comment on the JIRA. I
think your design is
> > > > promising.
> > > > > > One
> > > > > > > > >>>>  other thing I would suggest is for
us to crowd source
> > > > > > requirements
> > > > > > > > around
> > > > > > > > >>>>  model management. Specifically:
> > > > > > > > >>>>
> > > > > > > > >>>>  Model versioning and retention
> > > > > > > > >>>>  Model access controls management
> > > > > > > > >>>>  Requirements around concept drift
> > > > > > > > >>>>  Requirements around model output
> > > > > > > > >>>>  Any model audit and logging requirements
> > > > > > > > >>>>  What model metrics need to be exposed
> > > > > > > > >>>>  Requirements around failure modes
> > > > > > > > >>>>
> > > > > > > > >>>>  03.07.2016, 14:00, "Casey Stella"
<cestella@gmail.com
> >:
> > > > > > > > >>>>>  Hi all,
> > > > > > > > >>>>>
> > > > > > > > >>>>>  I think we are at the point
where we should try to
> > tackle
> > > > > Model
> > > > > > > as a
> > > > > > > > >>>>>  service for Metron. As such,
I created a JIRA and
> > proposed
> > > > an
> > > > > > > > >>>>  architecture
> > > > > > > > >>>>>  for accomplishing this within
Metron.
> > > > > > > > >>>>>
> > > > > > > > >>>>>  My inclination is to be data
science language/library
> > > > agnostic
> > > > > > and
> > > > > > > > to
> > > > > > > > >>>>>  provide a general purpose REST
infrastructure for
> > managing
> > > > and
> > > > > > > > serving
> > > > > > > > >>>>>  models trained on historical
data captured from
> Metron.
> > > The
> > > > > > > > assumption is
> > > > > > > > >>>>>  that we are within the hadoop
ecosystem, so:
> > > > > > > > >>>>>
> > > > > > > > >>>>>    - Models stored on HDFS
> > > > > > > > >>>>>    - REST Model Services resource-managed
via Yarn
> > > > > > > > >>>>>    - REST Model Services discovered
via Zookeeper.
> > > > > > > > >>>>>
> > > > > > > > >>>>>  I would really appreciate community
comment on the
> JIRA
> > (
> > > > > > > > >>>>>  https://issues.apache.org/jira/browse/METRON-265).
> The
> > > > > proposed
> > > > > > > > >>>>>  architecture is attached as
a document to that JIRA.
> > > > > > > > >>>>>
> > > > > > > > >>>>>  I look forward to feedback!
> > > > > > > > >>>>>
> > > > > > > > >>>>>  Best,
> > > > > > > > >>>>>
> > > > > > > > >>>>>  Casey
> > > > > > > > >>>>
> > > > > > > > >>>>  -------------------
> > > > > > > > >>>>  Thank you,
> > > > > > > > >>>>
> > > > > > > > >>>>  James Sirota
> > > > > > > > >>>>  PPMC- Apache Metron (Incubating)
> > > > > > > > >>>>  jsirota AT apache DOT org
> > > > > > > > >
> > > > > > > > >-------------------
> > > > > > > > >Thank you,
> > > > > > > > >
> > > > > > > > >James Sirota
> > > > > > > > >PPMC- Apache Metron (Incubating)
> > > > > > > > >jsirota AT apache DOT org
> > > > > > > >
> > > > > > >
> > > > > >
> > > > > >
> > > > > >
> > > > > > --
> > > > > > Thanks,
> > > > > > Andrew
> > > > > >
> > > > > > Subscribe to my book: Streaming Data <http://manning.com/psaltis
> >
> > > > > > <https://www.linkedin.com/pub/andrew-psaltis/1/17b/306>
> > > > > > twiiter: @itmdata <
> > > http://twitter.com/intent/user?screen_name=itmdata>
> > > > > >
> > > > >
> > > >
> > > >
> > > >
> > > > --
> > > > Thanks,
> > > > Andrew
> > > >
> > > > Subscribe to my book: Streaming Data <http://manning.com/psaltis>
> > > > <https://www.linkedin.com/pub/andrew-psaltis/1/17b/306>
> > > > twiiter: @itmdata <
> http://twitter.com/intent/user?screen_name=itmdata>
> > > >
> > >
> >
> >
> >
> > --
> > Thanks,
> > Andrew
> >
> > Subscribe to my book: Streaming Data <http://manning.com/psaltis>
> > <https://www.linkedin.com/pub/andrew-psaltis/1/17b/306>
> > twiiter: @itmdata <http://twitter.com/intent/user?screen_name=itmdata>
> >
>



-- 
Thanks,
Andrew

Subscribe to my book: Streaming Data <http://manning.com/psaltis>
<https://www.linkedin.com/pub/andrew-psaltis/1/17b/306>
twiiter: @itmdata <http://twitter.com/intent/user?screen_name=itmdata>

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