Silicon Valley
Past Presentations
Artwork Personalization @Netflix
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time. With a catalog spanning thousands of titles and a diverse member base spanning over a hundred million accounts, recommending the titles...
What We Got Wrong: Lessons From the Birth of Microservices
And, this is what happens if you build microservices and focus on computer science, and not focus on velocity. And I think you should understand what you're doing and make sure you focus on the velocity and keeping that checklist sane and reasonable and appropriate for the types of services that...
Jupyter Notebooks: Interactive Visualization Approaches
Jupyter Notebooks are becoming the IDE of choice for data scientists and researchers. They provide the users with a nice exploratory environment where they can quickly research and prototype different models and visualize the results all in one place. Notebooks are easy to share and can be...
Machine Learning for Handwriting and Sketch Recognition
The terms “big data”, “machine learning”, “neural networks”, and “deep learning” have appeared in many attention-grabbing headlines over the years, but what do they really mean? This presentation will describe some concrete examples of how they have impacted a variety of products,...
Scaling Slack - The Good, the Unexpected, and the Road Ahead
Slack is a persistent communication app for teams, with high customer expectations to deliver a reliable, rich, low latency client experience. Over the past couple of years, we've made major changes to the core service architecture to meet these needs for larger and larger enterprise...
Fairness, Transparency, and Privacy in AI @LinkedIn
How do we protect privacy of users in large-scale systems? How do we ensure fairness and transparency when developing machine learned models? With the ongoing explosive growth of AI/ML models and systems, these are some of the ethical and legal challenges encountered...
Interviews
Capacity Planning for Crypto Mania
What's the focus of the work that you do today?
Jordan: We’re on the Reliability Team at Coinbase. It was formed in response to the crazy spike of scaling challenges around 2017 with Cryptocurrency. The work is focused on traditional SRE topics of monitoring and instrumentation. We act as consultants for other mostly feature-focused teams. For example, we embed in teams to make...
Read Full Interviewnpm and the Future of JavaScript
Can you tell me more about what your talk is about?
I'll talk more about server-side stuff, and I’ll emphasize Node. We've found that the security message is important to people, so there will be quite a bit there. There's been this huge shift in how JavaScript is used and enterprises are only just beginning to catch up to that. There are many people working on JavaScript, and they're...
Read Full InterviewCRDTs in Production
What's the focus of the work that you're doing today?
This project on CRDT was my first project at PayPal and I was part of PD platform team. This is a team on top of infrastructure which provides services which will be used by product development engineers. Our platform allows us to drive the product development aligned with existing infrastructure and at the same time, we need to be...
Read Full InterviewOpen Source Robotics: Hands on with Gazebo and ROS 2
Tell us about the work that you do today.
I've been working at Open Robotics for almost four years now. Since the beginning, I was a contributor to the software. I started as an intern remotely. Since I joined I have been in the Gazebo team, which is the simulator team. That's where the big chunk of my work is, both on the simulator itself which is in C++ (I’ve worked on a...
Read Full InterviewYes, I Test In Production (And So Do You)
What's the motivation for this talk?
The motivation for this talk is to help people understand that deploying software carries an irreducible element of uncertainty and risk. Trying too hard to prevent failures will actually make your systems and your teams *more* vulnerable to failure and prolonged downtime. So what can you do about it?
Read Full InterviewHuman-Centric Machine Learning Infrastructure @Netflix
Can you give an example of some of the questions you get from data scientists when you are trying to deploy models?
When it comes to common questions, as boring as it may sound, my experience is that machine learning infrastructure is much more about data than science. Most questions we get are related to data: how do I find the data I need, how do I set up the data pipeline, how do I handle the somewhat non-trivial amounts of data in python and R,...
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