This talk will explore real-life experiences where cultural or organizational obstacles either blocked or helped reach good DevOps practices. There will be both successes and failures.

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In this talk, Antonio will showcase how powerful this approach is and how you can use it to find problems, and eventually resolve them.

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In this talk, Dotan will present OpenTelemetry, an ambitious open-source project with the promise of a unified framework for collecting observability data. With OpenTelemetry you could instrument your application in a vendor-agnostic way, and then analyze the telemetry data in your backend tool of choice, whether Prometheus, Jaeger, Zipkin, or others.

He will cover the current state of the various projects of OpenTelemetry (across programming languages, exporters, receivers, protocols), some of which are not even GA yet, and will provide useful guidance on how to get started with it.

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In this talk, Ed will cover the mistakes has made, what he has learned along the way, and how DevOps principles are bleeding over into the field of ML.

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Advancements in software development and testing have come a long way, however, there is still room for improvement. With new AI and ML algorithms spanning development, code reviews, unit testing, test authoring, and AIOps, teams can boost their productivity and deliver greater software faster. In this session, Eran will cover recommended areas where AI and ML can be leveraged, taken from his recent best-selling book — Accelerating Software Quality.

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In this talk, Feu will introduce different techniques and workflows with pros and cons and also will explain best practices that she has gathered in her training and from real-life experience over the years.

Additionally, Feu will throw in some tips on how to teach Git to others so the viewers can also pass on the knowledge.

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The world changed more as a result of COVID-19 than it did as a result of the September 11, 2001 attacks in the US in terms of the number of deaths and suffering of innocent people. Yet we took immediate action after the September 11 attacks and have kept up that vigilance for more than 2 decades. But after COVID-19, we are quickly trying to forget the misery and move on.

This talk will explore what they did wrong, and how they could have done better. But just because they missed the ball the first time around doesn’t mean that it’s too late to improve for the future. Let’s discuss how. 

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Think back to the time you had to use a self check out line at a grocery store. You bought groceries for potentially decades yet this experience brings butterflies to your stomach as the line builds behind you. In engineering, there has been a large push for self-service, especially around developer self-service in the name of engineering efficiency and removing bottlenecks. No more waiting weeks for VMs, you can dial up exactly what you need.

Learn in this session the pros and cons of self-service and having an Application-as-Code model in place can have a positive impact on Developer Experience and actually make self-service less angst causing. 

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In this session, Eric will show how to reduce cost and management overhead by moving ML inference to a serverless architecture. He will demonstrate building and deploying an ML inference project on serverless with infrastructure as code (IaC). Finally, Eric will discuss optimizing serverless compute for specific workloads and how to use cloud-native AI/ML services when possible.

At the end of this session, whether you’re a developer or a data scientist, you will have a basic understanding of how to create and deploy an inference engine using serverless technologies.

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In this talk, he will see how to design workloads around events and state machines, and how to deploy these applications into the AWS Cloud. James will walk through a live example and show common areas when custom code can often be removed. Finally, he will discuss best practices, some common pitfalls, and equip you with the practical knowledge to start using this approach in your serverless applications.

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