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Past Presentations

Models in Minutes not Months: AI as Microservices

Companies are redefining their businesses by building models and learning from data. Whether it is using data science to predict their best sales and marketing targets, automating digital customer interactions using bots, or reducing waste in logistics and manufacturing - Artificial Intelligence...

Sarah Aerni Senior Manager, Data Science @Salesforce
Servlet vs Reactive: Choosing the Right Stack

When Netflix upgraded their main gateway, serving 83 million users, from Servlet based, blocking Zuul 1 to the Netty based, non-blocking Zuul 2, the results were interesting and nuanced with benefits and trade-offs. Spring Framework 5 provides a similar choice with Servlet based Spring MVC...

Rossen Stoyanchev Spring Framework Committer @Pivotal
The Most Secure Program Is One That Doesn’t Exist

Rust is a programming language that started with the explicit goal of preventing segfaults and guaranteeing thread safety to create a fearlessly concurrent systems language. The compiler enforces type- and memory- safety to achieve this. There’s a significant overlap between critical security...

Diane Hosfelt Research Engineer @mozilla
Quantifying Risk

The FAIR methodology is an emerging standard for measuring information risks. But, it can be intimidating to get started with a risk quantification program, as people may be reluctant to to go beyond Low/Medium/High categories to real numbers. At Netflix, we have introduced risk quantification in...

Markus De Shon Sr. Security Engineer, Detection Engineering Lead @Netflix
Failure at Netflix Velocity

Netflix is a strong believer in Chaos Engineering and the Velocity of Innovation. Most of the time, our customers never notice the former and appreciate the latter. Occasionally however… Can not connect to Netflix. You press play and it doesn't work. You can't log in. Nothing is on the screen...

Dave Hahn Sr SRE, Reliability and Chaos Engineering @Netflix
Automating Netflix ML Pipelines With Meson

In this talk we discuss the evolution of ML automation at Netflix and how that lead us to build Meson, an orchestration system used for many of the personalization/recommendation algorithms. We will talk about challenges we faced, and what we learned automating thousands of ML pipelines with Meson.

Davis Shepherd ML Management @Netflix
Eugen Cepoi Senior Software Engineer @Netflix

Interviews

Szczepan Faber Mockito Creator, Core Eng Gradle 1.x/2.x, & TechLead @LinkedIn Development Tools

CI/CD: Lessons from LinkedIn and Mockito

What is your motivation for this talk?

In the open source and in the enterprise continuous delivery is not yet adopted as widely as it should given how it helps with productivity. I hope engineering teams will experiment more with continuous delivery and try to push themselves to go into that model.

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Justin Basilico Machine Learning Research/Engineering Director @Netflix

Artwork Personalization @Netflix

What work do you do at Netflix?

I lead one of the Machine Learning and Recommendation teams at Netflix. We're responsible for the end-to-end machine learning that decides what shows up on the Netflix homepage across all our different experiences. When you log into Netflix, my team is responsible for what rows of TV shows and movies you see on the homepage. We select...

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Alina Yurenko Developer Advocate for GraalVM @Oracle

JIT vs AOT Performance With GraalVM

What are the goals you have for the talk?

I want to offer an additional point of view on performance. Performance is something people often talk about but often focus on one or two metrics, and there might be more ways to look at it, and additional opportunities to optimize. We will go through some additional metrics, discuss how they are connected, and how you can optimize...

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Amitabha Roy Software Engineer @Google

Rethinking Applications for the NVM Era

What is your motivation for this talk?

So the focus of this talk is about how do you re-architect your software to take advantage of the advances of software today. Many developers treats the serialization of objects as sort of a separate process something that hangs off the side where the object is out of flat file or disk. But now if your object lives in persistent memory...

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Oleksandr Petrov Apache Cassandra Committer, Distributed Systems Engineer

Algorithms Behind Modern Storage Systems

What's the focus of the work that you're doing today?

I can tell you about Apache Cassandra and the patches I was working on recently. In Apache Cassandra, I've been recently working on transaction replication. Before that, I was working on SASI, an implementation of secondary indexes, on the commit log and on various storage and consistency related things. I had a chance to work on...

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Daniel Lemire Professor and Department Chair @TELUQ - Université du Québec

Parsing JSON Really Quickly: Lessons Learned

What is the work you're doing today?

I work on software performance from what I call a data engineering point of view. These are the kind of problems that interest me. You start with large volumes of data and processing the data itself is the bottleneck or indexing the data or crunching it or whatever you want to do with the data. 

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