covering the IBM Machine Learning Launch Event

brought to you by IBM.

Here are your hosts, Dave Vellante and Stu Miniman.

>> Good morning everybody, welcome to the Waldorf Astoria.

Stu Miniman and I are here in New York City,

the Big Apple,

for IBM's Machine Learning Event #IBMML.

We're fresh off Spark Summit, Stu,

where we had The Cube, this by the way is The Cube,

the worldwide leader in live tech coverage.

We were at Spark Summit last week,

George Gilbert and I,

watching the evolution of so-called big data.

Let me frame, Stu, where we're at

and bring you into the conversation.

The early days of big data were all about

offloading the data warehouse and reducing the cost

of the data warehouse.

I often joke that the ROI of big data

is reduction on investment, right?

There's these big, expensive data warehouses.

It was quite successful in that regard.

What then happened is we started to throw

all this data into the data warehouse.

People would joke it became a data swamp,

and you had a lot of tooling

to try to clean the data warehouse

and a lot of transforming and loading

and the ETL vendors started to participate there

in a bigger way.

Then you saw the extension of these data pipelines

to try to more with that data.

The Cloud guys have now entered in a big way.

We're now entering the Cognitive Era,

as IBM likes to refer to it.

Others talk about AI and machine learning

and deep learning,

and that's really the big topic here today.

What we can tell you, that the news goes out

at 9:00am this morning, and it was well known

that IBM's bringing machine learning

to its mainframe, z mainframe.

Two years ago, Stu, IBM announced the z13,

which was really designed to bring

analytic and transaction processing together

on a single platform.

Clearly IBM is extending the useful life

of the mainframe by bringing things like Spark,

certainly what it did with Linux

and now machine learning into z.

I want to talk about Cloud, the importance of Cloud,

and how that has really taken over the world of big data.

Virtually every customer you talk to now

is doing work on the Cloud.

It's interesting to see now

IBM unlocking its transaction base,

its mission-critical data,

to this machine learning world.

What are you seeing around Cloud and big data?

>> We've been digging into this big data space

since before it was called big data.

One of the early things that really got me

interested and exciting about it is,

from the infrastructure standpoint,

storage has always been one of its costs

that we had to have,

and the massive amounts of data,

the digital explosion we talked about,

is keeping all that information

or managing all that information

was a huge challenge.

Big data was really that bit flip.

How do we take all that information

and make it an opportunity?

How do we get new revenue streams?

Dave, IBM has been at the center of this

and looking at the higher-level pieces

of not just storing data, but leveraging it.

Obviously huge in analytics, lots of focus

on everything from Hadoop and Spark and newer technologies,

but digging in to how they can leverage up the stack,

which is where IBM has done a lot of acquisitions

in that space and leveraging that

and wants to make sure that they have a strong position

both in Cloud, which was renamed.

The soft layer is now IBM Bluemix

with a lot of services

including a machine learning service

that leverages the Watson technology

and of course OnPrem they've got the z

and the power solutions

that you and I have covered for many years

at the IBM Med show.

>> Machine learning obviously heavily leverages models.

We've seen in the early days of the data,

the data scientists would build models

and machine learning allows those models

to be perfected over time.

So there's this continuous process.

We're familiar with the world of Batch

and then some mini computer brought in

the world of interactive,

so we're familiar with those types of workloads.

Now we're talking about a new emergent workload

which is continuous.

Continuous apps where you're streaming data in,

what Spark is all about.

The models that data scientists are building

can constantly be improved.

The key is automation, right?

Being able to automate that whole process,

and being able to collaborate

between the data scientist, the data quality engineers,

even the application developers

that's something that IBM really tried to address

in its last big announcement in this area

of which was in October of last year

the Watson data platform,

what they called at the time the DataWorks.

So really trying to bring together

those different personas

in a way that they can collaborate together

and improve models on a continuous basis.

The use cases that you often hear in big data

and certainly initially in machine learning

are things like fraud detection.

Obviously ad serving has been a big data application

for quite some time.

In financial services, identifying good targets,

identifying risk.

What I'm seeing, Stu, is that the phase that we're in now

of this so-called big data and analytics world,

and now bringing in machine learning and deep learning,

is to really improve on some of those use cases.

For example, fraud's gotten much, much better.

Ten years ago, let's say, it took many, many months,

if you ever detected fraud.

Now you get it in seconds, or sometimes minutes,

but you also get a lot of false positives.

Oops, sorry, the transaction didn't go through.

Did you do this transaction?

Yes, I did.

Oh, sorry, you're going to have to redo it

because it didn't go through.

It's very frustrating for a lot of users.

That will get better and better and better.

We've all experienced retargeting from ads,

and we know how crappy they are.

That will continue to get better.

The big question that people have

and it goes back to Jeff Hammerbacher,

the best minds of my generation

are trying to get people to click on ads.

When will we see big data really start

to affect our lives in different ways

like patient outcomes?

We're going to hear some of that today

from folks in health care and pharma.

Again, these are the things that people are waiting for.

The other piece is, of course, IT.

What you're seeing, in terms of IT,

in the whole data flow?

>> Yes, a big question we have, Dave, is

where's the data?

And therefore, where does it make sense

to be able to do that processing?

In big data we talked about you've got

masses amounts of data,

can we move the processing to that data?

With IT, the day before, your RCTO talked that

there's going to be massive amounts of data at the edge

and I don't have the time or the bandwidth

or the need necessarily

to pull that back to some kind of central repository.

I want to be able to work on it there.

Therefore there's going to be a lot of data

worked at the edge.

Peter Levine did a whole video talking about how,

"Oh, Public Cloud is dead, it's all going to the edge."

A little bit hyperbolic to the statement

we understand that there's plenty use cases

for both Public Cloud and for the edge.

In fact we see Google big pushing machine learning

TensorFlow,

it's got one of those machine learning frameworks out there

that we expect a lot of people to be working on.

Amazon is putting effort into the MXNet framework,

which is once again an open-source effort.

One of the things I'm looking at the space,

and I think IBM can provide some leadership here

is to what frameworks are going to become popular

across multiple scenarios?

How many winners can there be for these frameworks?

We already have multiple programming languages,

multiple Clouds.

How much of it is just API compatibility?

How much of work there,

and where are the repositories of data going to be,

and where does it make sense to do that

predictive analytics, that advanced processing?

>> You bring up a good point.

Last year, last October, at Big Data CIV,

we had a special segment of data scientists

with a data scientist panel.

It was great.

We had some rockstar data scientists on there

like Dee Blanchfield and Joe Caserta,

and a number of others.

They echoed what you always hear

when you talk to data scientists.

"We spend 80% of our time messing with the data,

"trying to clean the data, figuring out the data quality,

"and precious little time on the models

"and proving the models

"and actually getting outcomes from those models."

So things like Spark have simplified that whole process

and unified a lot of the tooling

around so-called big data.

We're seeing Spark adoption increase.

George Gilbert in our part one and part two last week

in the big data forecast from Wikibon

showed that we're still not on the steep part

of the Se-curve, in terms of Spark adoption.

Generically, we're talking about streaming as well

included in that forecast,

but it's forecasting that increasingly those applications

are going to become more and more important.

It brings you back to what IBM's trying to do

is bring machine learning

into this critical transaction data.

Again, to me, it's an extension of the vision

that they put forth two years ago,

bringing analytic and transaction data together,

actually processing within that Private Cloud complex,

which is what essentially this mainframe is,

it's the original Private Cloud, right?

You were saying off-camera,

it's the original converged infrastructure.

It's the original Private Cloud.

>> The mainframe's still here, lots of Linux on it.

We've covered for many years,

you want your cool Linux docker, containerized,

machine learning stuff,

I can do that on the Zn-series.

>> You want Python and Spark and Re and Papa Java,

and all the popular programming languages.

It makes sense.

It's not like a huge growth platform,

it's kind of flat, down, up in the product cycle

but it's alive and well

and a lot of companies run their businesses

obviously on the Zn.

We're going to be unpacking that all day.