Scale By The Bay 2018: Chris Fregly Interview

so my name is Chris fregley I am the so my name is Chris fregley I am the founder and CEO at pipeline AI here in founder and CEO at pipeline AI here in San Francisco for focused on real-time San Francisco for focused on real-time machine learning and continuous training machine learning and continuous training continuous model improvement throughout continuous model improvement throughout the lifecycle of a model after they've the lifecycle of a model after they've been moved into production yeah the been moved into production yeah the streaming parts obviously the coolest streaming parts obviously the coolest thing the biggest differentiator is the thing the biggest differentiator is the ability to continually improve models ability to continually improve models online most people when you think of a online most people when you think of a machine learning company you think of machine learning company you think of offline batch SPARC workloads offline batch SPARC workloads distributed tensorflow offline but we're distributed tensorflow offline but we're basically bringing these pipelines live basically bringing these pipelines live and in production so bringing forth a and in production so bringing forth a lot of my old Netflix experience and my lot of my old Netflix experience and my day to Brick's experience and combining day to Brick's experience and combining those into one platform yeah and pipelines my company is very yeah and pipelines my company is very much on the edge of application much on the edge of application development so software development as development so software development as well as data pipelines and machine well as data pipelines and machine learning so really I've been bouncing in learning so really I've been bouncing in between those two so best practices between those two so best practices around the infrastructure and sort of around the infrastructure and sort of intelligent like infrastructure ways to intelligent like infrastructure ways to scale out your machine learning but not scale out your machine learning but not too aggressively ways to scale it down too aggressively ways to scale it down you know yeah just a lot of best you know yeah just a lot of best practices from these internal systems practices from these internal systems you see at uber Netflix you know Google you see at uber Netflix you know Google these kind of places yeah there's a lot of good talks there's yeah there's a lot of good talks there's a couple good talks on machine learning a couple good talks on machine learning in production so I'm gonna be paying in production so I'm gonna be paying attention to those there's some like attention to those there's some like newer open-source projects that are sort newer open-source projects that are sort of end to end machine learning that I'll of end to end machine learning that I'll be paying attention to be paying attention to so yeah yeah a lot of good stuff coming so yeah yeah a lot of good stuff coming out of Google in those folks these days you know I'm a traditional Java guy from you know I'm a traditional Java guy from back in the day so when I learned Scala back in the day so when I learned Scala it was it was pretty magical there's you it was it was pretty magical there's you know quite a lot of good Scala know quite a lot of good Scala frameworks as well - quite honestly frameworks as well - quite honestly since I've been doing more with machine since I've been doing more with machine learning I've been doing a lot of Python learning I've been doing a lot of Python and now that I'm you know we're kind of and now that I'm you know we're kind of coordinating the UI for our application coordinating the UI for our application we're doing quite a bit of nodejs and we're doing quite a bit of nodejs and javascript so you see functional javascript so you see functional principles throughout all these principles throughout all these different languages but yeah just the different languages but yeah just the readability the crispness of you know readability the crispness of you know the constructs the basic constructs the the constructs the basic constructs the concurrency constructs things like this let me see I love that it's right down let me see I love that it's right down the street from where I live so I could the street from where I live so I could pop in and out and I'm on my way to the pop in and out and I'm on my way to the gym right now so popped in for this gym right now so popped in for this interview and then a quick panel later interview and then a quick panel later today but yeah it's a lot of the you today but yeah it's a lot of the you know familiar faces so you know a lot of know familiar faces so you know a lot of people from all parts of my life from my people from all parts of my life from my Netflix life from the cassandra' world Netflix life from the cassandra' world you know all the way to my former you know all the way to my former roommate I think is sitting right behind roommate I think is sitting right behind you right there that works for Google so you right there that works for Google so yeah people everywhere [Music] you