incubator-general mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Hitesh Shah <hit...@apache.org>
Subject Re: [VOTE] Accept PredictionIO into the Apache Incubator
Date Tue, 24 May 2016 22:59:30 GMT
+1 (binding)

— Hitesh

> On 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)


---------------------------------------------------------------------
To unsubscribe, e-mail: general-unsubscribe@incubator.apache.org
For additional commands, e-mail: general-help@incubator.apache.org


Mime
View raw message