Infrastructure

We have all become cloud developers. Every day we use the cloud to supercharge our applications, deliver new capabilities, and reach scales previously unheard of. Leveraging the cloud effectively, however, means navigating and mastering the ever-expanding infrastructure landscape, including public cloud services for compute, data, and AI; containers, serverless, and Kubernetes; hybrid environments; and even SaaS -- often many at once. Watch these presentations and learn about the modern languages, tools, and techniques that leading-edge companies are using to innovate in this world of ever-increasing capabilities.

Past Presentations

Helm 3: A Mariner's Delight

Adjusting your spyglass and looking out over the water, you can see how useful a package manager like Helm is. Perhaps you’ve used it to manage the fractal complexity of packages on your Kubernetes clusters (without losing track of versions stashed in the hold). But Helm 3 is rumored to be...

Lachlan Evenson Principal Program Manager @Microsoft & K8s Release Lead for 1.16
AWS Cloud Development Kit (CDK)

The AWS Cloud Development Kit (CDK) allows you to describe your application’s infrastructure using a general-purpose programming language, such as TypeScript, JavaScript or Python. This opens up familiar avenues for working with your infrastructure, such as using your favorite...

Richard Boyd Developer Tooling Advocate @AWSCloud & CDK Core Contributor
CI/CD for Machine Learning

Machine Learning is now widely used across our industry, yet we have very limited tooling when it comes to automating the ML model versioning, testing, and release. We will show how a CI/CD pipeline for ML can greatly improve both your productivity and the reliability of your software.

Sasha Rosenbaum Program Manager on the Azure DevOps Engineering Team @Microsoft
Human-Centric Machine Learning Infrastructure @Netflix

Netflix has over 100 data scientists applying machine learning to a wide range of business problems from title popularity predictions to quality of streaming optimizations. Our unique culture gives data scientists plenty of freedom to choose the modeling approach, libraries, and even the...

Ville Tuulos Machine Learning Infrastructure Engineer @Netflix
Snowflake Architecture: Building a Data Warehouse for the Cloud

At Snowflake, we wanted to architect a data warehouse from the ground up to leverage all the benefits of the cloud. Unlike shared-storage architectures that tie storage and compute together, we built a single integrated system with fully independent scaling for compute, storage and services. In...

Thierry Cruanes Co-founder Snowflake Computing @SnowflakeDB
How to Invest in Technical Infrastructure

Deciding what to work on is always difficult and is especially treacherous for folks working as infrastructure engineers and leaders. Will Larson unpacks the process of picking and prioritizing technical infrastructure work, which is essential to long-term company success but discussed...

Will Larson Foundation Engineering @Stripe

Interviews

Sasha Rosenbaum Program Manager on the Azure DevOps Engineering Team @Microsoft

CI/CD for Machine Learning

What do you want people to leave the talk with?

If I had to summarize it in one line it would be any CI/CD pipeline is better than none. If you're going to automate major key pieces of this process will make your life a lot easier, simplify it and add speed to your deployments.

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Sangeeta Handa Engineering Manager, Billing Infrastructure @Netflix

Building Resilience in Production Migrations

What's the focus of the work that you do today?

I lead Billing Infrastructure Engineering at Netflix. We build the infrastructure that helps Netflix collect charges from its members. Part of that is to determine who should be charged and how much through our systems. We also hold all the gift codes and balances and track them. We also support major customer workflows. Our services...

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Charity Majors Co-Founder @Honeycombio, formerly DevOps @ParseIT/@Facebook

Yes, 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?

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Ville Tuulos Machine Learning Infrastructure Engineer @Netflix

Human-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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Ben Kehoe Cloud Robotics Research Scientist @iRobot

Serverless IoT @iRobot

What is the focus of your work today?

I help guide and shape our cloud architecture and how we integrate into the Smart home from a technology perspective—we are just about 100% Amazon.

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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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