+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) >
