+1 (binding) Ralph
> On May 24, 2016, at 3:44 AM, John D. Ament <johndam...@apache.org> wrote: > > +1 > > On Mon, May 23, 2016 at 6:23 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) >> --------------------------------------------------------------------- To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org For additional commands, e-mail: general-h...@incubator.apache.org
