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From Tsuyoshi Ozawa <oz...@apache.org>
Subject Re: [VOTE] Accept PredictionIO into the Apache Incubator
Date Wed, 25 May 2016 04:12:08 GMT
+1 (non-binding)
- Tsuyoshi

On Wed, May 25, 2016 at 1:00 PM, Reynold Xin <rxin@databricks.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)
>>

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