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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: [VOTE] Accept PredictionIO into the Apache Incubator
Date Mon, 23 May 2016 23:46:01 GMT
+1 (binding)



On Mon, May 23, 2016 at 7:31 PM, Debo Dutta (dedutta) <dedutta@cisco.com>
wrote:

> +1
>
>
>
>
> On 5/23/16, 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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