+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)
