Hi there. My name is Greg Ainslie-Malik,

and I'd like to take you on a really

brief tour

through Splunk's machine learning

toolkit.

Originally developed for what Gartner

termed citizen data scientists,

the machine learning toolkit presents a

whole host of

features for customers

mostly focused around assistance and

experiments

to help users who aren't familiar with

data science

train and test machine learning models

and deploy them into production.

And most of these assistants present as

kind of guided interfaces where you can

input some SPL, something that our users

are very familiar with,

select some algorithms, do some

pre-processing,

things that our users are less familiar

with, and then view a set of dashboards, a

set of reports that tell them about

their model's performance.

However, what we see from the telemetry

is that these experiments are generally

used as almost like pseudo training to help

users familiarize themselves with MLTK, but of

the monthly active users,

actually more than 95% of them run

MLTK searches straight from the search

bar.

So here you can see an example of that

where we're using the fit command

that ships with MLTK to apply an anomaly

detection search.

And you can see that this is actually

just two lines of SPL.

So for our NOC and SOC personas, those

who are very familiar to us

at Splunk, this is quite a simple thing

to do.

Now, while the search bar and the

experiments can help our users develop

and deploy

simple techniques like this for finding

anomalies or making predictions,

what we're starting to see is a trend

towards

use case focused workflows. Here we have

one for ITSI

where

more complex techniques can be run

against data without

having to see the details of the ML

that's being applied at all.

So here we have a list of episodes,

incidents in ITSI.

Where I'm clicking on an incident, some-

a technique called causal inference gets

run in the background

to determine the root cause of that

incident, and you can see here a graph

structure that has mapped out

those root cause relationships, and up

here you can see a table where

for the service that was impacted by the

incident,

here are all the KPIs that are affected

it. And I'm clicking in this,

we can quickly drill down and see what

the raw data looked like,

and I could draw the conclusion that

perhaps it was disk space used

that was the reason behind this incident

in this case.