Ron, If you are talking about Tensorflow Saved model format, I personally think that it is overkill for model serving. My preferred option is to used traditional TF export, which can be optimized for serving. As for processing I am using TF Java APIs, which basically is a population of the tensor column.
But if you are really interested, we can talk about it in San Jose or set up a config call if you want to discuss it sooner. Boris Lublinsky FDP Architect boris.lublin...@lightbend.com https://www.lightbend.com/ > On Jan 16, 2018, at 10:53 PM, Ron Gonzalez <zlgonza...@yahoo.com> wrote: > > Yes you're right. I believe this is the use case that I'm after. So if I > understand correctly, transforms that do aggregations just assume that the > batch of data being aggregated is passed as part of a tensor column. Is it > possible to hook up a lookup call to another Tensorflow Serving servable for > a join in batch mode? > > Will a saved model when loaded into a tensorflow serving model actually have > the definitions of the metadata when retrieved using the tensorflow serving > metadata api? > > Thanks, > Ron > > On Tuesday, January 16, 2018, 6:16:01 PM PST, Charles Chen <c...@google.com> > wrote: > > > This sounds similar to the use case for tf.Transform, a library that depends > on Beam: https://github.com/tensorflow/transform > <https://github.com/tensorflow/transform> > On Tue, Jan 16, 2018 at 5:51 PM Ron Gonzalez <zlgonza...@yahoo.com > <mailto:zlgonza...@yahoo.com>> wrote: > Hi, > I was wondering if anyone has encountered or used Beam in the following > manner: > > 1. During machine learning training, use Beam to create the event table. > The flow may consist of some joins, aggregations, row-based transformations, > etc... > 2. Once the model is created, deploy the model to some scoring service via > PMML (or some other scoring service). > 3. Enable the SAME transformations used in #1 by using a separate engine > but thereby guaranteeing that it will transform the data identically as the > engine used in #1. > > I think this is a pretty interesting use case where Beam is used to > guarantee portability across engines and deployment (batch to true streaming, > not micro-batch). What's not clear to me is with respect to how batch joins > would translate during one-by-one scoring (probably lookups) or how > aggregations given that some kind of history would need to be stored (and how > much is kept is configurable too). > > Thoughts? > > Thanks, > Ron
