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Maturity ORYX

avatar
Explorer

Oryx are mature enough to be implemented in a large company?

It is still in the construction / development process.

I need an architecture to recommend products to customers etc.

 

What are the biggest differences between Samoa and ORYX?

1 ACCEPTED SOLUTION

avatar
Master Collaborator
It depends a lot on what you mean by "mature". It is mature in the
sense that it's:

- a fifth (!) generation architecture
- used by real customers in production, yes
- not at a beta stage, but at a 2.1.1 release now
- built on technologies that Cloudera supports as production ready

it's not mature in the sense that it's:

- is not itself formally supported by Cloudera -- only a labs project
- is still fairly new in its current 2.x form, having finished about 5
months ago

I think the architecture is certainly the way to go if you're building
this kind of thing on Hadoop. See http://oryx.io.

I don't know much about Samoa but I understand it to be a distributed
stream-centric ML library. On the plus side, it's probably better than
anything in Spark for building huge models incrementally, as this is
what it focuses on. On the downside, it doesn't do the model serving
element, which Oryx tries to provide, and in a sense Samoa is a much
less standard technology than Spark, HDFS, and Kafka.

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3 REPLIES 3

avatar
Master Collaborator
It depends a lot on what you mean by "mature". It is mature in the
sense that it's:

- a fifth (!) generation architecture
- used by real customers in production, yes
- not at a beta stage, but at a 2.1.1 release now
- built on technologies that Cloudera supports as production ready

it's not mature in the sense that it's:

- is not itself formally supported by Cloudera -- only a labs project
- is still fairly new in its current 2.x form, having finished about 5
months ago

I think the architecture is certainly the way to go if you're building
this kind of thing on Hadoop. See http://oryx.io.

I don't know much about Samoa but I understand it to be a distributed
stream-centric ML library. On the plus side, it's probably better than
anything in Spark for building huge models incrementally, as this is
what it focuses on. On the downside, it doesn't do the model serving
element, which Oryx tries to provide, and in a sense Samoa is a much
less standard technology than Spark, HDFS, and Kafka.

avatar
Explorer

Thank you so much. I will study more about Oryx.
Do you know more sources to study ORYX?

avatar
Master Collaborator
The best resource is probably the web site at http://oryx.io as well
as the source code. http://github.com/OryxProject/oryx