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so<00:00:14.080><c> my</c><00:00:14.349><c> name</c><00:00:14.500><c> is</c><00:00:14.740><c> Chris</c><00:00:15.099><c> fregley</c><00:00:15.490><c> I</c><00:00:16.180><c> am</c><00:00:16.930><c> the</c>

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so my name is Chris fregley I am the
 

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so my name is Chris fregley I am the
founder<00:00:17.349><c> and</c><00:00:17.560><c> CEO</c><00:00:18.099><c> at</c><00:00:18.520><c> pipeline</c><00:00:18.730><c> AI</c><00:00:19.180><c> here</c><00:00:19.750><c> in</c>

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founder and CEO at pipeline AI here in
 

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founder and CEO at pipeline AI here in
San<00:00:20.320><c> Francisco</c><00:00:20.800><c> for</c><00:00:21.550><c> focused</c><00:00:21.910><c> on</c><00:00:22.119><c> real-time</c>

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San Francisco for focused on real-time
 

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San Francisco for focused on real-time
machine<00:00:22.900><c> learning</c><00:00:23.259><c> and</c><00:00:23.939><c> continuous</c><00:00:25.019><c> training</c>

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machine learning and continuous training
 

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machine learning and continuous training
continuous<00:00:26.830><c> model</c><00:00:27.550><c> improvement</c><00:00:28.300><c> throughout</c>

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continuous model improvement throughout
 

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continuous model improvement throughout
the<00:00:29.230><c> lifecycle</c><00:00:29.589><c> of</c><00:00:29.890><c> a</c><00:00:30.009><c> model</c><00:00:30.339><c> after</c><00:00:30.609><c> they've</c>

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the lifecycle of a model after they've
 

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the lifecycle of a model after they've
been<00:00:31.509><c> moved</c><00:00:31.689><c> into</c><00:00:31.869><c> production</c><00:00:38.730><c> yeah</c><00:00:39.760><c> the</c>

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been moved into production yeah the
 

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been moved into production yeah the
streaming<00:00:41.079><c> parts</c><00:00:41.409><c> obviously</c><00:00:41.799><c> the</c><00:00:41.949><c> coolest</c>

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streaming parts obviously the coolest
 

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streaming parts obviously the coolest
thing<00:00:42.430><c> the</c><00:00:43.420><c> biggest</c><00:00:43.720><c> differentiator</c><00:00:44.080><c> is</c><00:00:44.559><c> the</c>

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thing the biggest differentiator is the
 

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thing the biggest differentiator is the
ability<00:00:44.890><c> to</c><00:00:45.330><c> continually</c><00:00:46.330><c> improve</c><00:00:46.720><c> models</c>

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ability to continually improve models
 

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ability to continually improve models
online<00:00:48.809><c> most</c><00:00:49.809><c> people</c><00:00:50.140><c> when</c><00:00:50.379><c> you</c><00:00:50.500><c> think</c><00:00:50.559><c> of</c><00:00:50.830><c> a</c>

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online most people when you think of a
 

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online most people when you think of a
machine<00:00:51.190><c> learning</c><00:00:51.309><c> company</c><00:00:51.729><c> you</c><00:00:52.000><c> think</c><00:00:52.210><c> of</c>

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machine learning company you think of
 

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machine learning company you think of
offline<00:00:52.690><c> batch</c><00:00:53.220><c> SPARC</c><00:00:54.220><c> workloads</c>

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offline batch SPARC workloads
 

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offline batch SPARC workloads
distributed<00:00:55.989><c> tensorflow</c><00:00:56.320><c> offline</c><00:00:57.150><c> but</c><00:00:58.150><c> we're</c>

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distributed tensorflow offline but we're
 

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distributed tensorflow offline but we're
basically<00:00:59.830><c> bringing</c><00:01:00.189><c> these</c><00:01:00.430><c> pipelines</c><00:01:00.909><c> live</c>

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basically bringing these pipelines live
 

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basically bringing these pipelines live
and<00:01:01.839><c> in</c><00:01:02.260><c> production</c><00:01:02.500><c> so</c><00:01:03.489><c> bringing</c><00:01:03.879><c> forth</c><00:01:04.119><c> a</c>

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and in production so bringing forth a
 

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and in production so bringing forth a
lot<00:01:04.360><c> of</c><00:01:04.420><c> my</c><00:01:04.629><c> old</c><00:01:04.809><c> Netflix</c><00:01:05.260><c> experience</c><00:01:05.729><c> and</c><00:01:06.729><c> my</c>

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lot of my old Netflix experience and my
 

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lot of my old Netflix experience and my
day<00:01:06.939><c> to</c><00:01:07.090><c> Brick's</c><00:01:07.330><c> experience</c><00:01:07.900><c> and</c><00:01:08.229><c> combining</c>

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day to Brick's experience and combining
 

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day to Brick's experience and combining
those<00:01:09.220><c> into</c><00:01:09.640><c> one</c><00:01:10.360><c> platform</c>

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yeah<00:01:22.789><c> and</c><00:01:23.090><c> pipelines</c><00:01:23.659><c> my</c><00:01:24.560><c> company</c><00:01:24.950><c> is</c><00:01:25.100><c> very</c>

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yeah and pipelines my company is very
 

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yeah and pipelines my company is very
much<00:01:25.640><c> on</c><00:01:25.880><c> the</c><00:01:26.119><c> edge</c><00:01:26.149><c> of</c><00:01:26.630><c> application</c>

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much on the edge of application
 

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much on the edge of application
development<00:01:27.380><c> so</c><00:01:28.039><c> software</c><00:01:28.369><c> development</c><00:01:28.939><c> as</c>

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development so software development as
 

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development so software development as
well<00:01:29.840><c> as</c><00:01:30.079><c> data</c><00:01:30.770><c> pipelines</c><00:01:31.250><c> and</c><00:01:31.520><c> machine</c>

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well as data pipelines and machine
 

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well as data pipelines and machine
learning<00:01:31.819><c> so</c><00:01:32.450><c> really</c><00:01:33.319><c> I've</c><00:01:33.439><c> been</c><00:01:33.469><c> bouncing</c><00:01:33.979><c> in</c>

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learning so really I've been bouncing in
 

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learning so really I've been bouncing in
between<00:01:34.429><c> those</c><00:01:34.610><c> two</c><00:01:42.640><c> so</c><00:01:43.640><c> best</c><00:01:44.600><c> practices</c>

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between those two so best practices
 

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between those two so best practices
around<00:01:45.350><c> the</c><00:01:45.950><c> infrastructure</c><00:01:46.549><c> and</c><00:01:47.770><c> sort</c><00:01:48.770><c> of</c>

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around the infrastructure and sort of
 

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around the infrastructure and sort of
intelligent<00:01:49.520><c> like</c><00:01:49.819><c> infrastructure</c><00:01:51.130><c> ways</c><00:01:52.130><c> to</c>

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intelligent like infrastructure ways to
 

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intelligent like infrastructure ways to
scale<00:01:53.239><c> out</c><00:01:53.509><c> your</c><00:01:53.989><c> machine</c><00:01:54.289><c> learning</c><00:01:54.890><c> but</c><00:01:55.189><c> not</c>

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scale out your machine learning but not
 

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scale out your machine learning but not
too<00:01:55.700><c> aggressively</c><00:01:56.470><c> ways</c><00:01:57.470><c> to</c><00:01:57.709><c> scale</c><00:01:57.890><c> it</c><00:01:58.099><c> down</c>

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too aggressively ways to scale it down
 

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too aggressively ways to scale it down
you<00:01:59.569><c> know</c><00:01:59.659><c> yeah</c><00:01:59.989><c> just</c><00:02:00.200><c> a</c><00:02:00.259><c> lot</c><00:02:00.349><c> of</c><00:02:00.470><c> best</c>

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you know yeah just a lot of best
 

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you know yeah just a lot of best
practices<00:02:00.649><c> from</c><00:02:01.310><c> these</c><00:02:01.549><c> internal</c><00:02:02.270><c> systems</c>

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practices from these internal systems
 

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practices from these internal systems
you<00:02:02.899><c> see</c><00:02:03.079><c> at</c><00:02:03.229><c> uber</c><00:02:03.849><c> Netflix</c><00:02:04.849><c> you</c><00:02:05.810><c> know</c><00:02:05.899><c> Google</c>

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you see at uber Netflix you know Google
 

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you see at uber Netflix you know Google
these<00:02:06.349><c> kind</c><00:02:06.619><c> of</c><00:02:06.649><c> places</c>

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yeah<00:02:17.420><c> there's</c><00:02:17.840><c> a</c><00:02:17.930><c> lot</c><00:02:18.050><c> of</c><00:02:18.170><c> good</c><00:02:18.290><c> talks</c><00:02:18.530><c> there's</c>

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yeah there's a lot of good talks there's
 

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yeah there's a lot of good talks there's
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a couple good talks on machine learning
 

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a couple good talks on machine learning
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in production so I'm gonna be paying
 

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in production so I'm gonna be paying
attention<00:02:23.300><c> to</c><00:02:23.750><c> those</c><00:02:23.930><c> there's</c><00:02:24.230><c> some</c><00:02:24.500><c> like</c>

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attention to those there's some like
 

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attention to those there's some like
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newer open-source projects that are sort
 

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newer open-source projects that are sort
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of end to end machine learning that I'll
 

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of end to end machine learning that I'll
be<00:02:29.210><c> paying</c><00:02:29.390><c> attention</c><00:02:29.540><c> to</c>

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be paying attention to
 

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be paying attention to
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so yeah yeah a lot of good stuff coming
 

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so yeah yeah a lot of good stuff coming
out<00:02:32.570><c> of</c><00:02:32.630><c> Google</c><00:02:33.020><c> in</c><00:02:33.140><c> those</c><00:02:33.290><c> folks</c><00:02:33.560><c> these</c><00:02:33.710><c> days</c>

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you<00:02:46.770><c> know</c><00:02:46.890><c> I'm</c><00:02:47.070><c> a</c><00:02:47.100><c> traditional</c><00:02:47.670><c> Java</c><00:02:47.880><c> guy</c><00:02:48.120><c> from</c>

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you know I'm a traditional Java guy from
 

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you know I'm a traditional Java guy from
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back in the day so when I learned Scala
 

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back in the day so when I learned Scala
it<00:02:52.170><c> was</c><00:02:52.410><c> it</c><00:02:53.280><c> was</c><00:02:53.550><c> pretty</c><00:02:53.700><c> magical</c><00:02:53.960><c> there's</c><00:02:54.960><c> you</c>

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it was it was pretty magical there's you
 

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it was it was pretty magical there's you
know<00:02:55.500><c> quite</c><00:02:55.740><c> a</c><00:02:55.770><c> lot</c><00:02:55.980><c> of</c><00:02:56.010><c> good</c><00:02:56.250><c> Scala</c>

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know quite a lot of good Scala
 

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know quite a lot of good Scala
frameworks<00:02:57.030><c> as</c><00:02:57.180><c> well</c><00:02:57.330><c> -</c><00:02:58.130><c> quite</c><00:02:59.130><c> honestly</c>

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frameworks as well - quite honestly
 

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frameworks as well - quite honestly
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since I've been doing more with machine
 

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since I've been doing more with machine
learning<00:03:01.020><c> I've</c><00:03:01.440><c> been</c><00:03:01.560><c> doing</c><00:03:01.770><c> a</c><00:03:01.860><c> lot</c><00:03:02.040><c> of</c><00:03:02.070><c> Python</c>

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learning I've been doing a lot of Python
 

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learning I've been doing a lot of Python
and<00:03:03.440><c> now</c><00:03:04.440><c> that</c><00:03:04.650><c> I'm</c><00:03:04.770><c> you</c><00:03:05.070><c> know</c><00:03:05.190><c> we're</c><00:03:05.490><c> kind</c><00:03:05.730><c> of</c>

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and now that I'm you know we're kind of
 

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and now that I'm you know we're kind of
coordinating<00:03:06.330><c> the</c><00:03:06.360><c> UI</c><00:03:06.870><c> for</c><00:03:07.080><c> our</c><00:03:07.380><c> application</c>

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coordinating the UI for our application
 

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coordinating the UI for our application
we're<00:03:08.160><c> doing</c><00:03:08.400><c> quite</c><00:03:08.520><c> a</c><00:03:08.580><c> bit</c><00:03:08.670><c> of</c><00:03:08.790><c> nodejs</c><00:03:09.270><c> and</c>

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we're doing quite a bit of nodejs and
 

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we're doing quite a bit of nodejs and
javascript<00:03:10.260><c> so</c><00:03:10.560><c> you</c><00:03:11.190><c> see</c><00:03:11.370><c> functional</c>

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javascript so you see functional
 

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javascript so you see functional
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principles throughout all these
 

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principles throughout all these
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different languages but yeah just the
 

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different languages but yeah just the
readability<00:03:15.900><c> the</c><00:03:16.520><c> crispness</c><00:03:17.520><c> of</c><00:03:17.970><c> you</c><00:03:18.240><c> know</c>

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readability the crispness of you know
 

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readability the crispness of you know
the<00:03:19.920><c> constructs</c><00:03:20.850><c> the</c><00:03:21.060><c> basic</c><00:03:21.360><c> constructs</c><00:03:21.780><c> the</c>

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the constructs the basic constructs the
 

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the constructs the basic constructs the
concurrency<00:03:22.980><c> constructs</c><00:03:23.400><c> things</c><00:03:23.700><c> like</c><00:03:23.880><c> this</c>

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let<00:03:35.790><c> me</c><00:03:35.880><c> see</c><00:03:36.150><c> I</c><00:03:36.390><c> love</c><00:03:36.630><c> that</c><00:03:36.690><c> it's</c><00:03:36.960><c> right</c><00:03:37.080><c> down</c>

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let me see I love that it's right down
 

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let me see I love that it's right down
the<00:03:37.380><c> street</c><00:03:37.410><c> from</c><00:03:37.740><c> where</c><00:03:37.830><c> I</c><00:03:37.890><c> live</c><00:03:38.100><c> so</c><00:03:38.370><c> I</c><00:03:38.520><c> could</c>

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the street from where I live so I could
 

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the street from where I live so I could
pop<00:03:38.910><c> in</c><00:03:39.060><c> and</c><00:03:39.210><c> out</c><00:03:39.300><c> and</c><00:03:39.510><c> I'm</c><00:03:40.020><c> on</c><00:03:40.530><c> my</c><00:03:40.590><c> way</c><00:03:40.680><c> to</c><00:03:40.830><c> the</c>

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pop in and out and I'm on my way to the
 

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pop in and out and I'm on my way to the
gym<00:03:41.190><c> right</c><00:03:41.370><c> now</c><00:03:41.430><c> so</c><00:03:41.850><c> popped</c><00:03:42.630><c> in</c><00:03:42.780><c> for</c><00:03:42.960><c> this</c>

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gym right now so popped in for this
 

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gym right now so popped in for this
interview<00:03:43.470><c> and</c><00:03:43.710><c> then</c><00:03:43.830><c> a</c><00:03:44.220><c> quick</c><00:03:45.030><c> panel</c><00:03:45.360><c> later</c>

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interview and then a quick panel later
 

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interview and then a quick panel later
today<00:03:45.720><c> but</c><00:03:45.870><c> yeah</c><00:03:46.800><c> it's</c><00:03:47.220><c> a</c><00:03:47.280><c> lot</c><00:03:47.400><c> of</c><00:03:47.460><c> the</c><00:03:47.670><c> you</c>

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today but yeah it's a lot of the you
 

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today but yeah it's a lot of the you
know<00:03:48.030><c> familiar</c><00:03:48.210><c> faces</c><00:03:48.630><c> so</c><00:03:49.050><c> you</c><00:03:49.560><c> know</c><00:03:49.830><c> a</c><00:03:49.860><c> lot</c><00:03:49.950><c> of</c>

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know familiar faces so you know a lot of
 

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know familiar faces so you know a lot of
people<00:03:50.310><c> from</c><00:03:50.930><c> all</c><00:03:51.930><c> parts</c><00:03:52.380><c> of</c><00:03:52.440><c> my</c><00:03:52.530><c> life</c><00:03:52.740><c> from</c><00:03:52.950><c> my</c>

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people from all parts of my life from my
 

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people from all parts of my life from my
Netflix<00:03:53.460><c> life</c><00:03:53.670><c> from</c><00:03:54.120><c> the</c><00:03:54.330><c> cassandra'</c><00:03:54.870><c> world</c>

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Netflix life from the cassandra' world
 

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Netflix life from the cassandra' world
you<00:03:56.490><c> know</c><00:03:56.580><c> all</c><00:03:56.760><c> the</c><00:03:56.880><c> way</c><00:03:56.970><c> to</c><00:03:57.030><c> my</c><00:03:57.900><c> former</c>

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you know all the way to my former
 

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you know all the way to my former
roommate<00:03:58.530><c> I</c><00:03:58.710><c> think</c><00:03:59.010><c> is</c><00:03:59.100><c> sitting</c><00:03:59.370><c> right</c><00:03:59.490><c> behind</c>

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roommate I think is sitting right behind
 

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roommate I think is sitting right behind
you<00:03:59.880><c> right</c><00:04:00.030><c> there</c><00:04:00.240><c> that</c><00:04:00.390><c> works</c><00:04:00.720><c> for</c><00:04:00.900><c> Google</c><00:04:01.200><c> so</c>

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you right there that works for Google so
 

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you right there that works for Google so
yeah<00:04:02.010><c> people</c><00:04:02.220><c> everywhere</c>

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[Music]

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you