+1 (binding) On Mon, May 23, 2016 at 6:32 PM, Luciano Resende <luckbr1...@gmail.com> wrote:
> +1 (binding) > > On Mon, May 23, 2016 at 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) > > > > > > -- > Luciano Resende > http://twitter.com/lresende1975 > http://lresende.blogspot.com/ >
