Hi Nick, Unless there are any concerns or objections, I will add you and Mr. Dusenberry to the proposal as initial committers tomorrow.
Everyone, As it seems that discussion has died down I plan to start a VOTE thread on this coming Monday. Thank you for the comment and attention thus far. On Tue, May 17, 2016 at 12:58 PM, Nick Pentreath <nick.pentre...@gmail.com> wrote: > Hi there > > I'm glad to see the proposal to incubate PredictionIO. In my previous life > as a startup co-founder, I kept a close eye on the project, and it would be > fantastic to see it become an Apache incubating project! > > The folks working on Apache Spark and Apache SystemML (incubating) here at > IBM are excited about the possibilities for integrating PredictionIO and > SystemML (Mike Dusenberry is a committer on that project), as well > as further improving Spark integration (I'm a PMC member on that project). > > Mike and I, together with Luciano (who is a mentor on this proposal) would > like to volunteer our services as initial committers, if that is agreeable. > > Kind regards > Nick > mln...@apache.org > > > > > > > ---------- Forwarded message ---------- > > From: Andrew Purtell <apurt...@apache.org> > > To: "general@incubator.apache.org" <general@incubator.apache.org> > > Cc: > > Date: Fri, 13 May 2016 13:41:38 -0700 > > Subject: [DISCUSS] PredictionIO incubation proposal > > Greetings, > > > > It is my pleasure to > > > > propose the PredictionIO project for incubation at the Apache Software > > Foundation. > > > > PredictionIO is a > > popular > > open > > > > source Machine Learning Server built on top of a state-of-the-art open > > source stack, including several Apache technologies, that > > > > enables developers to manage and deploy production-ready predictive > > services for various kinds of machine learning tasks > > , with more than 400 production deployments around the world and a > growing > > contributor community. > > > > > > The text of the proposal is included below and is also available at > > https://wiki.apache.org/incubator/PredictionIO > > > > Best regards, > > Andrew Purtell > > > > > > = 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. > > > > You can find the Template Gallery at https://templates.prediction.io/ > > > > === 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 > > 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 > > > > === 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 > > > > === 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)
