
Microsoft launches a drag-and-drop machine learning tool - Anon84
https://techcrunch.com/2019/05/02/microsoft-launches-a-drag-and-drop-machine-learning-tool-and-hosted-jupyter-notebooks/
======
ungzd
Nothing shocking. GUI for machine learning existed for decades, for example
Weka and Rapidminer. I don't see "drag and drop" in screenshots, however; it's
just awful gadget journalism lingo, common for this site with reviews of
gaming mouses.

ETL-style dataflow pipelines are more natural for such tasks than imperative
programming, it's not just "for people who can't code". Rapidminer is
dataflow-based too.

Actual links, if you want to avoid gadget news website:

[https://docs.microsoft.com/en-us/azure/machine-
learning/serv...](https://docs.microsoft.com/en-us/azure/machine-
learning/service/how-to-create-portal-experiments)

[https://docs.microsoft.com/en-us/azure/machine-
learning/serv...](https://docs.microsoft.com/en-us/azure/machine-
learning/service/concept-automated-ml)

~~~
scottlocklin
Yann LeCun and Leon Bottou had one of these in Lush ... I think back in the
80s. It's still in there!

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cwyers
So, I clicked on the link to understand how this was different than their
existing drag-and-drop machine learning tool, and...

> This tool, the Azure Machine Learning visual interface, looks suspiciously
> like the existing Azure ML Studio, Microsoft’s first stab at building a
> visual machine learning tool. Indeed, the two services look identical. The
> company never really pushed this service, though, and almost seemed to have
> forgotten about it despite the fact that it always seemed like a really
> useful tool for getting started with machine learning.

> Microsoft says this new version combines the best of Azure ML Studio with
> the Azure Machine Learning service. In practice, this means that while the
> interface is almost identical, the Azure Machine Learning visual interface
> extends what was possible with ML Studio by running on top of the Azure
> Machine Learning service and adding that services’ security, deployment and
> life cycle management capabilities.

The answer is "not much."

------
blackflame7000
The problem with this approach is that so much of machine learning is
dependent on the datasets you choose to give it. If people need their hand
held through setting up a basic Neural Network, I foresee a lot of garbage in
garbage out

~~~
marshray
This sentiment gets expressed every time programming is made more accessible.

It _always_ turns out that the difficulty of "setting up a basic [hello world
application]" is entirely unrelated to the essential complexity of the problem
space and attracting a broader range of new users is later viewed as a
valuable advance.

~~~
blackflame7000
Well for example if people don't understand the difference between continuous,
discrete, and unrelated values then they will have major flaws. For example,
say they are trying to build a NN to predict customer orders by geographic
area. If they treat zip codes as continuous or discrete values they're going
to get really strange results because ML is ultimately just
interpolation/extrapolation. Idk how well drag and drop can convey those
principles

~~~
jmngomes
I see user skills and the fact that the tool is drag and drop as two different
issues, in this context.

A fluent Python developer that doesn't understand basic ML concepts can easily
use something like Scikit to code and build "wrong" models. By "basic
concepts" I mean standard tasks like data preparation for specific algorithms,
sampling, evaluation methods, testing for bias, or just generally how to
properly execute most ML tasks like the ones prescribed by something like
CRISP-DM.

Someone with basic coding skills - e.g. knows SQL and some imperative
programming - but with a solid understanding of ML tasks, and how to execute
them properly, probably has a better chance of coming up with better results
than the former, using something like IBM Modeller or RapidMiner.

Note that I'm not saying that a drag and drop tool is superior; you could
build a flow-based GUI for Scikit, so a tool like this is always, at most, an
interface to some code libraries (Scikit, in this example). Having full access
to the actual lib, or just better libs, is likely to be less constraining, and
more apt for more sophisticated approaches.

------
pazimzadeh
Is this related to their acquisition of Lobe?
[https://techcrunch.com/2018/09/13/microsoft-acquires-
lobe-a-...](https://techcrunch.com/2018/09/13/microsoft-acquires-lobe-a-drag-
and-drop-ai-tool/)

[https://lobe.ai/](https://lobe.ai/)

If not, it looks like they have parallel projects working on basically the
same thing. I’ve been waiting for Microsoft to open Lobe to the public..

~~~
ickler9
No, it's not. And agreed, Lobe has always looked great. Hopefully there will
be announcements about it at Build next week.

------
dlkf
How many people are there that satisfy both of the following criteria:

1\. They want to build, train, and deploy a machine learning model into
production. Presumably as a microservice, part of a web application, etc

2\. They don't know how to program

I honestly can't imagine a less useful product than drag and drop ML.

~~~
mabbo
I want to build, train and deploy a ML learning model in production.

I do know how to program.

I still want this tool. Badly. So badly. Just because I _can_ program doesn't
mean I want to use programming to solve every problem. I want simple tools
that do the complex things for me for the 90% of cases where they are good
enough.

Let me spend my time on the 10% of problems that simple tools can't solve!

~~~
jorblumesea
ML is so complicated and so black box-y, it's not like it's a todo list or
something.

It seems like this would be the worst of both worlds. Too simple to be of any
use to any ML engineer, too complicated for the uninitiated, not customizable
enough for a domain expert.

~~~
yeahitslikethat
This is the general response from programmers toward tools that automate
programming. It's also generally FUD about being replaced by said tools.

~~~
jorblumesea
I don't think ML is a regular form of programming.

~~~
haggy
That's because it's not programming. ML describes a strategy using machines to
derive information from data. You use programming languages to tell machines
how to execute on that strategy.

------
kerng
Nice. Microsoft has been doing some great work the last couple of years with
ML - especially for beginners its great to get started with their tooling. I
always like their UI and workflow parts in Azure ML.

I still use their free Jupyter Notebooks service also.

------
hestefisk
By the way, I can see a massive adoption of this in consulting. Often we
develop ‘quick and dirty’ model to test variables or do a high level
regression / predictive model. Having this without the need for Python code is
extremely useful, especially when you work in short term, high burn projects
and just need an 80/20 answer.

------
behnamoh
I wonder who their target market are. ML requires a solid math background and
the ability to customize every detail of the process which drag-and-drop tools
never fully provide. I understand that it's always beneficial to have more
user-friendly tools, but I still think ML experts - at least those who don't
just copy-paste code snippets from SO - would still prefer the more
professional R and Python packages.

Maybe Microsoft aims at _teaching_ ML to beginners, which still would be
detrimental if they get used to just that.

~~~
bdod6
I had this exact same thought when I read the headline. It seems like MS and
others are viewing ML as a similar opportunity to Big Data/BI ten years ago.
You saw the "democratization of data" as people with little technical skills
could suddenly create analytics dashboards within tools like Tableau.

In my opinion, it's far too easy to make a critical mistake during
design/implementation of ML to follow this same path. And what's more, if you
mess up making an analytics dashboard, it's usually fairly obvious. In ML,
there are MANY ways to mess up a model and you have no easy way to tell.

If someone doesn't have the technical experience behind creating these models,
I would not trust any output they give me from using one of these tools. And
if they do have the experience, they would certainly not be choosing to use
one of these tools either.

~~~
streetcat1
Can you please elaborate more on what kind of critical mistakes a machine can
make, while someone with math background would not make.

I am building a competing tool, so I am not affiliate with MS, but I do think
that auto ML has value.

Machine learning is different from imperative programming in such that most of
the "programming" is done by experiments and not with actual "program", hence
there is an opportunity to replace programming with compute. I.e. an automl
platform can create 100's of models/pipelines and just try them all.

Also, why would you trust a model which was created manually and not a model
which was auto created.

When a model is created in auto ML it pass the same validation process as
manually created model, so in both cases the quality of the model should be
judged independent from the way that it was created.

In addition, all models (regardless of how they were created - human / not
human), should be monitored for predictive performance. I.e. I will not
"trust" any model without continuous verification.

~~~
gidim
A common error is target leaking. An AutoML system will likely consider this a
"strong feature". This is where having someone that actually understands the
business domain is critical.

There's no question that there's value in AutoML system yet most ML production
systems I've worked on / seen were way more complex than feature vector ->
model -> prediction. You likely have multiple models, pipelines,
normalizations and plain old conditionals. Hard to automate all of this.

~~~
streetcat1
Right. I am aiming at the group of companies that have 0 data scientist and
would like to avoid hiring one. I assume that their use cases is simple/common
and can be automated.

Note that automation is not only building the model, but automating the full
life cycle - pre processing, hp optimization , pipeline deployment and
monitoring/retraining.

------
leblancfg
I was taking a look at H2O.ai's autoML dashboard yesterday, which they call
Driverless AI. Broader in scope, includes an interpretability feature, and
seems a little less white box-ey than what I could see from MS. Plus, great
looks. Haven't tried it first-hand, but I did take a mental note.

[https://www.h2o.ai/products/h2o-driverless-
ai/](https://www.h2o.ai/products/h2o-driverless-ai/)

------
hestefisk
Imagine the auto ML built into Excel. Now that would be awesome.

------
simmers
That iris data set is perhaps the most prolific data in the world.

------
siliconc0w
I think an ideal generalized ML service is more like - you give it a CSV and
then add another row with missing column(s) and it guesses what should be in
those column(s) along with some human readable explanation of how it got
there.

~~~
cbHXBY1D
Azure Machine Learning actually does this:

[https://docs.microsoft.com/en-us/azure/machine-
learning/serv...](https://docs.microsoft.com/en-us/azure/machine-
learning/service/how-to-transform-data)

------
visarga
So it's like Scratch for ML? You 'draw' a program with a unwieldy graphical
interface, and when it inevitably becomes a visual mess of complexity what can
you do?

------
dessant
Is downloading the trained model for offline use possible?

------
sgt101
Because drag and drop ML is so new.

[https://en.wikipedia.org/wiki/SPSS_Modeler](https://en.wikipedia.org/wiki/SPSS_Modeler)

I remember using this in NINETEEN NINETY FIVE (it was called clementine then).

Interestingly one of the leads (Rob Milne) sold up (to IBM , forced sale I
guess due to a cash squeeze and no investors) and went to Everest, where he
got to the bottom of the Hilary step, had a massive heart attack and died.

Makes ya thunk.

~~~
hawaiianbrah
A major company launching something doesn’t mean it’s a new idea?

~~~
alvatech
I agree. The headline didn't say that Microsoft invented drag and drop ML

