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From James Taylor <jamestay...@apache.org>
Subject Re: [VOTE] Accept PredictionIO into the Apache Incubator
Date Mon, 23 May 2016 22:32:52 GMT
+1 (binding)

On Mon, May 23, 2016 at 3:32 PM, Luciano Resende <luckbr1975@gmail.com>
wrote:

> +1 (binding)
>
> On Mon, May 23, 2016 at 3:22 PM, Andrew Purtell <apurtell@apache.org>
> wrote:
>
> > Since discussion on the matter of PredictionIO has died down, I would
> like
> > to call a VOTE
> > on accepting PredictionIO into the Apache Incubator.
> >
> > Proposal: https://wiki.apache.org/incubator/PredictionIO
> >
> > ​[ ] +1 Accept PredictionIO into the Apache Incubator
> > [ ] +0 Abstain
> > [ ] -1 Do not accept PredictionIO into the Apache Incubator, because ...
> >
> > This vote will be open for at least 72 hours.
> >
> > My vote is +1 (binding)
> >
> > --
> >
> > PredictionIO Proposal
> >
> > Abstract
> >
> > PredictionIO is an open source Machine Learning Server built on top of
> > state-of-the-art open source stack, that enables developers to manage and
> > deploy production-ready predictive services for various kinds of machine
> > learning tasks.
> >
> > Proposal
> >
> > The PredictionIO platform consists of the following components:
> >
> >    * PredictionIO framework - provides the machine learning stack for
> >      building, evaluating and deploying engines with machine learning
> >      algorithms. It uses Apache Spark for processing.
> >
> >    * Event Server - the machine learning analytics layer for unifying
> > events
> >      from multiple platforms. It can use Apache HBase or any JDBC
> backends
> >      as its data store.
> >
> > The PredictionIO community also maintains a Template Gallery, a place to
> > publish and download (free or proprietary) engine templates for different
> > types of machine learning applications, and is a complemental part of the
> > project. At this point we exclude the Template Gallery from the proposal,
> > as it has a separate set of contributors and we’re not familiar with an
> > Apache approved mechanism to maintain such a gallery.
> >
> > Background
> >
> > PredictionIO was started with a mission to democratize and bring machine
> > learning to the masses.
> >
> > Machine learning has traditionally been a luxury for big companies like
> > Google, Facebook, and Netflix. There are ML libraries and tools lying
> > around the internet but the effort of putting them all together as a
> > production-ready infrastructure is a very resource-intensive task that is
> > remotely reachable by individuals or small businesses.
> >
> > PredictionIO is a production-ready, full stack machine learning system
> that
> > allows organizations of any scale to quickly deploy machine learning
> > capabilities. It comes with official and community-contributed machine
> > learning engine templates that are easy to customize.
> >
> > Rationale
> >
> > As usage and number of contributors to PredictionIO has grown bigger and
> > more diverse, we have sought for an independent framework for the project
> > to keep thriving. We believe the Apache foundation is a great fit.
> Joining
> > Apache would ensure that tried and true processes and procedures are in
> > place for the growing number of organizations interested in contributing
> > to PredictionIO. PredictionIO is also a good fit for the Apache
> foundation.
> > PredictionIO was built on top of several Apache projects (HBase, Spark,
> > Hadoop). We are familiar with the Apache process and believe that the
> > democratic and meritocratic nature of the foundation aligns with the
> > project goals.
> >
> > Initial Goals
> >
> > The initial milestones will be to move the existing codebase to Apache
> and
> > integrate with the Apache development process. Once this is accomplished,
> > we plan for incremental development and releases that follow the Apache
> > guidelines, as well as growing our developer and user communities.
> >
> > Current Status
> >
> > PredictionIO has undergone nine minor releases and many patches.
> > PredictionIO is being used in production by Salesforce.com as well as
> many
> > other organizations and apps. The PredictionIO codebase is currently
> > hosted at GitHub, which will form the basis of the Apache git repository.
> >
> > Meritocracy
> >
> > We plan to invest in supporting a meritocracy. We will discuss the
> > requirements in an open forum. We intend to invite additional developers
> > to participate. We will encourage and monitor community participation so
> > that privileges can be extended to those that contribute.
> >
> > Community
> >
> > Acceptance into the Apache foundation would bolster the already strong
> > user and developer community around PredictionIO. That community includes
> > many contributors from various other companies, and an active mailing
> list
> > composed of hundreds of users.
> >
> > Core Developers
> >
> > The core developers of our project are listed in our contributors and
> > initial PPMC below. Though many are employed at Salesforce.com, there are
> > also engineers from ActionML, and independent developers.
> >
> > Alignment
> >
> > The ASF is the natural choice to host the PredictionIO project as its
> goal
> > is democratizing Machine Learning by making it more easily accessible to
> > every user/developer. PredictionIO is built on top of several top level
> > Apache projects as outlined above.
> >
> > Known Risks
> >
> > Orphaned Products
> >
> > PredictionIO has a solid and growing community. It is deployed on
> > production environments by companies of all sizes to run various kinds of
> > predictive engines.
> >
> > In addition to the community contribution to PredictionIO framework, the
> > community is also actively contributing new engines to the Template
> > Gallery as well as SDKs and documentation for the project. Salesforce is
> > committed to utilize and advance the PredictionIO code base and support
> > its user community.
> >
> > Inexperience with Open Source
> >
> > PredictionIO has existed as a healthy open source project for almost two
> > years and is the most starred Scala project on GitHub. All of the
> proposed
> > committers have contributed to ASF and Linux Foundation open source
> > projects. Several current committers on Apache projects and Apache
> Members
> > are involved in this proposal and intend to provide mentorship.
> >
> > Homogeneous Developers
> >
> > The initial list of committers includes developers from several
> > institutions, including Salesforce, ActionML, Channel4, USC as well as
> > unaffiliated developers.
> >
> > Reliance on Salaried Developers
> >
> > Like most open source projects, PredictionIO receives substantial support
> > from salaried developers. PredictionIO development is partially supported
> > by Salesforce.com, but there are many contributors from various other
> > companies, and an active mailing list composed of hundreds of users. We
> > will continue our efforts to ensure stewardship of the project to be
> > independent of salaried developers by meritocratically promoting those
> > contributors to committers.
> >
> > Relationships with Other Apache Product
> >
> > PredictionIO relies heavily on top level Apache projects such as Apache
> > Spark, HBase and Hadoop. However it brings a distinguished functionality,
> > rather than just an abstraction - Machine Learning in a plug-and-play
> > fashion.
> >
> > Compared to Apache Mahout, which focuses on the development of a wide
> > variety of algorithms, PredictionIO offers a platform to manage the whole
> > machine learning workflow, including data collection, data preparation,
> > modeling, deployment and management of predictive services in production
> > environments.
> >
> > An Excessive Fascination with the Apache Brand
> >
> > PredictionIO is already a widely known open source project. This proposal
> > is not for the purpose of generating publicity. Rather, the primary
> > benefits to joining Apache are those outlined in the Rationale section.
> >
> > Documentation
> >
> > PredictionIO boasts rich and live documentation, included in the code
> repo
> > (docs/manual directory), is built with Middleman, and publicly hosted at
> > https://docs.prediction.io
> >
> > Initial Source and Intellectual Property Submission Plan
> >
> > Currently, the PredictionIO codebase is distributed under the Apache 2.0
> > License and hosted on GitHub:
> https://github.com/PredictionIO/PredictionIO
> >
> > External Dependencies
> >
> > PredictionIO has the following external dependencies:
> >  * Apache Hadoop 2.4.0 (optional, required only if YARN and HDFS are
> > needed)
> >  * Apache Spark 1.3.0 for Hadoop 2.4
> >  * Java SE Development Kit 8
> >  * and one of the following sets:
> >    * PostgreSQL 9.1
> >  or
> >    * MySQL 5.1
> >  or
> >    * Apache HBase 0.98.6
> >    * Elasticsearch 1.4.0
> >
> > Upon acceptance to the incubator, we would begin a thorough analysis of
> > all transitive dependencies to verify this information and introduce
> > license checking into the build and release process by integrating with
> > Apache RAT.
> >
> > Cryptography
> >
> > PredictionIO does not include cryptographic code. We utilize standard
> > JCE and JSSE APIs provided by the Java Runtime Environment.
> >
> > Required Resources
> >
> > We request that following resources be created for the project to use
> >
> > Mailing lists
> >
> >   predictionio-private@incubator.apache.org (with moderated
> subscriptions)
> >   predictionio-dev
> >   predictionio-user
> >   predictionio-commits
> >
> >   We will migrate the existing PredictionIO mailing lists.
> >
> > Git repository
> >
> >   The PredictionIO team would like to use Git for source control, due to
> > our
> >   current use of GitHub.
> >
> >   git://git.apache.org/incubator-predictionio
> >
> > Documentation
> >
> >   https://predictionio.incubator.apache.org/docs/
> >
> > JIRA instance
> >
> >   PredictionIO currently uses the GitHub issue tracking system associated
> >   with its repository:
> https://github.com/PredictionIO/PredictionIO/issues
> > .
> >   We will migrate to Apache JIRA.
> >
> >   JIRA PREDICTIONIO
> >   https://issues.apache.org/jira/browse/PREDICTIONIO
> >
> > Other Resources
> >
> >   TravisCI for builds and test running.
> >
> >   PredictionIO's documentation, included in the code repo (docs/manual
> >   directory), is built with Middleman and publicly hosted at
> >   https://docs.prediction.io
> >
> >   A blog to drive adoption and excitement at https://blog.prediction.io
> >
> > Initial Committers
> >
> >   Pat Ferrell
> >   Tamas Jambor
> >   Justin Yip
> >   Xusen Yin
> >   Lee Moon Soo
> >   Donald Szeto
> >   Kenneth Chan
> >   Tom Chan
> >   Simon Chan
> >   Marco Vivero
> >   Matthew Tovbin
> >   Yevgeny Khodorkovsky
> >   Felipe Oliveira
> >   Vitaly Gordon
> >   Alex Merritt
> >
> > Affiliations
> >
> >   Pat Ferrell - ActionML
> >   Tamas Jambor - Channel4
> >   Justin Yip - independent
> >   Xusen Yin - USC
> >   Lee Moon Soo - NFLabs
> >   Donald Szeto - Salesforce
> >   Kenneth Chan - Salesforce
> >   Tom Chan - Salesforce
> >   Simon Chan - Salesforce
> >   Marco Vivero - Salesforce
> >   Matthew Tovbin - Salesforce
> >   Yevgeny Khodorkovsky - Salesforce
> >   Felipe Oliveira - Salesforce
> >   Vitaly Gordon - Salesforce
> >   Alex Merritt - ActionML
> >
> > Sponsors
> >
> > Champion
> >
> >   Andrew Purtell <apurtell at apache dot org>
> >
> > Nominated Mentors
> >
> >   Andrew Purtell <apurtell at apache dot org>
> >   James Taylor <jtaylor at apache dot org>
> >   Lars Hofhansl <larsh at apache dot org>
> >   Suneel Marthi <smarthi at apache dot org>
> >   Xiangrui Meng <meng at apache dot org>
> >   Luciano Resende <lresende at apache dot org>
> >
> > Sponsoring Entity
> >
> >   Apache Incubator PMC
> >
> >
> > --
> > Best regards,
> >
> >    - Andy
> >
> > Problems worthy of attack prove their worth by hitting back. - Piet Hein
> > (via Tom White)
> >
>
>
>
> --
> Luciano Resende
> http://twitter.com/lresende1975
> http://lresende.blogspot.com/
>

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