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From Asher Krim <>
Subject Re: MLlib mission and goals
Date Tue, 24 Jan 2017 20:17:43 GMT
On the topic of usability, I think more effort should be put into large
scale testing. We've encountered issues with building large models that are
not apparent in small models, and these issues have made productizing
ML/MLLIB much more difficult than we first anticipated. Considering that
one of the biggest selling points for Spark is ease of scaling to large
datasets, I think fleshing out SPARK-15573 and testing large models should
be a priority

On Tue, Jan 24, 2017 at 2:23 PM, Miao Wang <> wrote:

> I started working on ML/MLLIB/R since last year. Here are some of my
> thoughts from a beginner's perspective:
> Current ML/MLLIB core algorithms can serve as good implementation
> examples, which makes adding new algorithms easier. Even a beginner like
> me, can pick it up quickly and learn how to add new algorithms. So, adding
> new algorithms should not be a barrier for developers who really need
> specific algorithms and it should not be the first priority in ML/MLLIB
> long term goal. We should only add highly demanded algorithms. I hope there
> will be detailed JIRA/email discussions to decide whether we want to accept
> a new algorithm.
> I strongly agree that we should improve ML/MLLIB usability, stability and
> performance in core algorithms and the foundations such as linear algebra
> library etc. This will keep Spark ML/MLLIB competitive in the area of
> machine learning framework. For example, Microsoft just open source a fast,
> distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART)
> framework based on decision tree algorithms. The performance and accuracy
> is much better than xboost. We need to follow up and improve Spark GBT
> alogrithms in near future.
> Another related area is SparkR. API Parity between SparkR and ML/MLLIB is
> important. We should also pay attention to R users' habits and experiences
> when maintaining API parity.
> Miao
> ----- Original message -----
> From: Stephen Boesch <>
> To: Sean Owen <>
> Cc: "" <>
> Subject: Re: MLlib mission and goals
> Date: Tue, Jan 24, 2017 4:42 AM
> re:  and "Would these really be better in the core
> project?"   That was not at all the intent of my input: instead to ask "how
> and where to structure/place deployment quality code that yet were *not*
> part of the distribution?"   The spark packages has no curation whatsoever
> : no minimum standards of code quality and deployment structures, let alone
> qualitative measures of usefulness.
> While spark packages would never rival CRAN and friends there is not even
> any mechanism in place to get started.  From the CRAN site:
>    Even at the current growth rate of several packages a day, all
> submissions are still rigorously quality-controlled using strong testing
> features available in the R system .
> Maybe give something that has a subset of these processes a try ?
> Different folks than are already over-subscribed in MLlib ?
> 2017-01-24 2:37 GMT-08:00 Sean Owen <>:
> My $0.02, which shouldn't be weighted too much.
> I believe the mission as of Spark ML has been to provide the framework,
> and then implementation of 'the basics' only. It should have the tools that
> cover ~80% of use cases, out of the box, in a pretty well-supported and
> tested way.
> It's not a goal to support an arbitrarily large collection of algorithms
> because each one adds marginally less value, and IMHO, is proportionally
> bigger baggage, because the contributors tend to skew academic, produce
> worse code, and don't stick around to maintain it.
> The project is already generally quite overloaded; I don't know if there's
> bandwidth to even cover the current scope. While 'the basics' is a
> subjective label, de facto, I think we'd have to define it as essentially
> "what we already have in place" for the foreseeable future.
> That the bits on aren't so hot is not a problem but a
> symptom. Would these really be better in the core project?
> And, or: I entirely agree with Joseph's take.
> On Tue, Jan 24, 2017 at 1:03 AM Joseph Bradley <>
> wrote:
> This thread is split off from the "Feedback on MLlib roadmap process
> proposal" thread for discussing the high-level mission and goals for
> MLlib.  I hope this thread will collect feedback and ideas, not necessarily
> lead to huge decisions.
> Copying from the previous thread:
> *Seth:*
> """
> I would love to hear some discussion on the higher level goal of Spark
> MLlib (if this derails the original discussion, please let me know and we
> can discuss in another thread). The roadmap does contain specific items
> that help to convey some of this (ML parity with MLlib, model persistence,
> etc...), but I'm interested in what the "mission" of Spark MLlib is. We
> often see PRs for brand new algorithms which are sometimes rejected and
> sometimes not. Do we aim to keep implementing more and more algorithms? Or
> is our focus really, now that we have a reasonable library of algorithms,
> to simply make the existing ones faster/better/more robust? Should we aim
> to make interfaces that are easily extended for developers to easily
> implement their own custom code (e.g. custom optimization libraries), or do
> we want to restrict things to out-of-the box algorithms? Should we focus on
> more flexible, general abstractions like distributed linear algebra?
> I was not involved in the project in the early days of MLlib when this
> discussion may have happened, but I think it would be useful to either
> revisit it or restate it here for some of the newer developers.
> """
> *Mingjie:*
> """
> +1 general abstractions like distributed linear algebra.
> """
> I'll add my thoughts, starting with our past *t**rajectory*:
> * Initially, MLlib was mainly trying to build a set of core algorithms.
> * Two years ago, the big effort was adding Pipelines.
> * In the last year, big efforts have been around completing Pipelines and
> making the library more robust.
> I agree with Seth that a few *immediate goals* are very clear:
> * feature parity for DataFrame-based API
> * completing and improving testing for model persistence
> * Python, R parity
> *In the future*, it's harder to say, but if I had to pick my top 2 items,
> I'd list:
> *(1) Making MLlib more extensible*
> It will not be feasible to support a huge number of algorithms, so
> allowing users to customize their ML on Spark workflows will be critical.
> This is IMO the most important thing we could do for MLlib.
> Part of this could be building a healthy community of Spark Packages, and
> we will need to make it easier for users to write their own algorithms and
> packages to facilitate this.  Part of this could be allowing users to
> customize existing algorithms with custom loss functions, etc.
> *(2) Consistent improvements to core algorithms*
> A less exciting but still very important item will be constantly improving
> the core set of algorithms in MLlib. This could mean speed, scaling,
> robustness, and usability for the few algorithms which cover 90% of use
> cases.
> There are plenty of other possibilities, and it will be great to hear the
> community's thoughts!
> Thanks,
> Joseph
> --
> Joseph Bradley
> Software Engineer - Machine Learning
> Databricks, Inc.
> [image:] <>
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Asher Krim
Senior Software Engineer

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