+1 (non-binding) On Mon, May 23, 2016 at 5:46 PM Henry Saputra <henry.sapu...@gmail.com> wrote:
> +1 (binding) > > On Mon, May 23, 2016 at 4:46 PM, Ted Dunning <ted.dunn...@gmail.com> > wrote: > > > +1 (binding) > > > > > > > > On Mon, May 23, 2016 at 7:31 PM, Debo Dutta (dedutta) <dedu...@cisco.com > > > > wrote: > > > > > +1 > > > > > > > > > > > > > > > On 5/23/16, 3:22 PM, "Andrew Purtell" <apurt...@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-priv...@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) > > > > > >
