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From Priyank Ashok Rastogi <>
Subject RE: [VOTE] Accept PredictionIO into the Apache Incubator
Date Tue, 24 May 2016 05:17:29 GMT
+1 Accept PredictionIO into the Apache Incubator

-----Original Message-----
From: Andrew Purtell [] 
Sent: 24 May 2016 03:52
Subject: [VOTE] Accept PredictionIO into the Apache Incubator

Since discussion on the matter of PredictionIO has died down, I would like to call a VOTE
on accepting PredictionIO into the Apache Incubator.


​[ ] +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


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.


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.


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.


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

Current Status

PredictionIO has undergone nine minor releases and many patches.
PredictionIO is being used in production by 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.


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.


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, there are also engineers from ActionML, and independent


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, 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.


PredictionIO boasts rich and live documentation, included in the code repo (docs/manual directory),
is built with Middleman, and publicly hosted at

Initial Source and Intellectual Property Submission Plan

Currently, the PredictionIO codebase is distributed under the Apache 2.0 License and hosted
on GitHub:

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
   * MySQL 5.1
   * 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.


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 (with moderated subscriptions)

  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.



JIRA instance

  PredictionIO currently uses the GitHub issue tracking system associated
  with its repository:
  We will migrate to Apache JIRA.


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

  A blog to drive adoption and excitement at

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


  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



  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)
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