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From Ashish <paliwalash...@gmail.com>
Subject Re: [VOTE] Accept PredictionIO into the Apache Incubator
Date Mon, 23 May 2016 22:33:38 GMT
+1 (non-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)



-- 
thanks
ashish

Blog: http://www.ashishpaliwal.com/blog
My Photo Galleries: http://www.pbase.com/ashishpaliwal

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