+1 (binding) On Tue, May 24, 2016 at 12:22 AM, 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) > -- Sergio Fernández Partner Technology Manager Redlink GmbH m: +43 6602747925 e: sergio.fernan...@redlink.co w: http://redlink.co
