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Hi, my name is Gleb Krivosheev, and I'm
a Principle Engineering Manager at Machine Learning Group at Microsoft.
>> Hi, my name is Yunling Wang,
I'm a Technical Program Manager in the same team.
>> So today we're gonna talk about Microsoft ML,
which is the ML package for Microsoft R Server.
We're bringing the best of scalable,
fast, battle tested algorithms to the Microsoft R Server.
So there are several things that I'm gonna talk about today.
And one of the key benefits of this package is that we're bringing fast
and scalable machine learning algorithms.
And they can be used for a variety of tasks.
And we have worked really hard to make them work really well within
several areas, specifically with text, also with images.
And with this release, we're bringing the Cross Platform aspect
so that all that technology is now available, not only on Windows but
also on Linux, on Spark and Hadoop.
And you can actually scale it out on the cluster.
All right, and let's talk about the learners that we have.
So imagine that you have a problem where you're trying to separate two
classes.
In this picture, they're represented in red and blue color.
So what you could do, you could kind of draw a line between them,
try to fit that line at the best as you could to separate it.
And that's what linear learners do.
We have two of them, we have FastLinear and LogisticRegession.
And they also support regularization,
which is called L1 and L2, which produces more accurate and
models that are more resistant to overfitting.
They're very fast to train, the models are very simple and
they can scale to a larger amount of data.
However, if you're interested more in accuracy and
you're trying to do a better job of trying to predict the results,
you often would go for boosted trees or random forests.
And then we have both FastTree, which is a boosted decision tree
which is similar to XGBoost and we also have a Random Forest as well.
And finally there is GPU-accerated deep neural net and a OneClassSvm.
OneClassSvm allows you to detect anomalies and
find outliers within your data.
Now let's talk a bit about scalability.
So, one of the issues with Open Source R
machine learning packages is that they're often limited to one thread,
one processor, and are limited to the data that you have.
So the advantage of the learners that we offer is that they are able
to stream data, meaning that you're not limited to RAM.
You can even train a one terabyte data set on one machine.
You can also are able to train on very large number of features,
actually billions of different features.
And that might come in handy if you have a very large product catalog or
if you're dealing with natural text where there's a lot of different
words or combinations of words you're trying to deal with.
But even if you have a scalable algorithm,
you also want to make it performant.
And so that's why we introduced support for Hadoop and Spark.
And so now those algorithms can be distributed on a cluster.
And you can reduce the time to train the model.
So the very nice thing about it is that you don't have to change
the code very much to take advantage of the scalability.
You just need to change the context from local to Spark and that
will allow you to take advantage of all this hardware, okay?
So now Yunling is gonna talk about the text analytics.
>> Thank you, Gleb.
Hi, so there are a lot of scenarios where you can imagine text analytics
being very useful.
For instance, in a generic text classification case,
you can imagine a customer support center receiving tons of
support tickets and email phone calls everyday.
And it will be naturally for them to want to classify them into
predefined categories so that it will be easier for
the support team to take simple actions on them.
And in another case,
you can image that they wanted to triage the support tickets based on
automatically detecting the severity of them and to detecting whether or
not it's likely to get escalated to a higher level of tier support.
Another interesting case is sentiment analysis.
You can imagine that when a company first launch a new product,
they would like to learn about the customer feedback to that product.
And one way to do that is to monitoring the social media and
to figure out if the tweet for the product was positive or
negative in the prospective sentiment.
Now we are able to achieve all of that by leveraging the natural
process language processing technology from Office and Bing.
And so for instance you will have access to the same
technologies that process millions and billions of web
requests on Bing everyday, if you'll choose our offering. So-
>> So a question.
So now you have this text processing.
But it is quite difficult to do sentiment because you have to have
all this training data and labels, and how do you do this?
>> Yes, so that's what I want to talk about next, because that is
something that we have been working on a lot in the latest release.
So we solve this problem by offering a pre-trained model.
So the pre-trained model allow you get a sentiment score out of the box
without having to have any training data.
So you can see in the code snippet below, you can very
easily get a sentiment score with a very simple few lines of code.
And the model is very high quality.
Because it is the same model that you will get
from a cognitive service sentiment analysis service.
The model also uses deep neural network
technologies in the process of training.
And this is a technology that we have obtained from
Microsoft research in Asia.
And finally, you can adopt this model to a custom domain
by using the pretrained model's score as a feature and
train the model on your domain data.
So Gleb, so far so
much about the text analytics, would you like to talk more about images?
>> Thanks Yunling.
So image featurization and
processing of images comes in a variety of situations.
Often you are trying to find a product in the catalog and
you don't know what it's called, but you know what it looks like.
Imagine you saw a shoe in the magazine and
you wanna buy the same shoe, how do you know where to find it?
So the solution is called image search, and
that uses actual similarity of images as opposed to any kind
of a textual description of it.
So that's one application.
The other one is if you try to
classify images into different groups.
So for example, if you are working on wood processing company, and
you're processing logs,
you want to identify what species of trees that they were made of, and
maybe what kind of quality to assign to each one.
You can also do it in science, for example.
Identifying types of galaxies,
or counting the different species of plankton that may
be very critical to understand the health of our ecosystems.
These are the very difficult problems to automate.
Unless you have the technology for processing images.
And finally in manufacturing,
often you're trying to detect anomalies and defects in forests.
And you want to do it at high volume, and high speed, and
high accuracy.
Image featurization and
training deep neural nets is actually very difficult.
It requires knowledge, it requires highly paid data scientists,
lots of hardware, lots of data and lots of time.
So we've made it very easy
by supplying those models with our package.
And so you can actually take the award-winning ResNet and
AlexNet models that we already have trained.
We've spent weeks and months of effort to do this.
And then they come pre-packaged and ready to use.
So the way it works is basically that you can take this model and
then you can apply it as a featurizer.
In other words, it would take an image and
produce a set of numeric features and then you can either search for
similar images or you can use a tree or
a linear classifier to then solve the problem you're trying to do.
Okay so, let's recap.
>> Sounds good.
So to summarize, in April release of Microsoft Machine Learning we will
have support for Hadoop and Spark via Emsembling.
We will also have a collection of pre-trained deep neural network
models and that includes Image Featurizers and
pre-trained sentiment Analysis models.
Now thank you very much.
>> Thank you so much.
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