Damian Brady
Staff Developer Advocate at GitHub
Brisbane, Australia
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Damian is part of Developer Advocacy team at GitHub and loves all things DevOps.
Formerly a Cloud Advocate at Microsoft for 4 years, and prior to that a dev at Octopus Deploy (https://octopus.com) and a Microsoft MVP, Damian has a 20+ year background in software development and consulting in a broad range of industries.
Damian regularly speak sat conferences, User Groups, and other events around the world.
Most of the time you'll find Damian talking to developers, IT Pros, and data scientists to help them get the most out of their DevOps and MLOps strategies.
Area of Expertise
Topics
Coding in the Age of AI
The world of software engineering has changed forever. In only a couple of years, generative AI has gone from a fun experiment to a vital tool for modern software developers. And while AI won't replace developers any time soon, devs who can effectively use AI tools will be far happier, more productive and in demand. So let's get productive!
We'll start by exploring how generative AI tools work under the hood, using GitHub Copilot as an example. We'll talk about prompt engineering in the context of writing code, and how you can get the best results to speed up your work. We'll also explore where these tools tend to fail, and what they excel at. You might be surprised by what they can do if used properly!
Your Code is just a Detail
We love code. We love new languages and language features, elegant architectures and ingenious algorithms. We love it so much that sometimes we get blinded by the pizazz and forget about the all the other parts of writing software beyond the code; the countless hidden dangers that can sink your software project.
In this session, we'll look up from the keyboard and broaden our horizons. We'll look at common bottlenecks, tools we take for granted, processes we don't think about, and relationships that go unspoken. We’ll focus on the bigger picture and see how going outside the confines of code can make us more valuable to the companies we work for and the teams we work with. Code is great, but it’s only a detail in a much larger organism.
GitHub Copilot - how it works, how we got here, and where it's going
GitHub Copilot is an AI pair programmer that helps you write code faster and with less work. Trained on billions of lines of code, it allows you to spend less time writing boilerplate and repetitive code, and more time solving bigger problems.
In practice, it can seem downright prescient, anticipating what you want. But how does it do that?
In this session, we'll look at what Copilot is (and what it's not), and how it works. We'll talk about how it was created, how it's being improved, and a little about the future of tools like Copilot.
Effective DevOps for Organizations
DevOps is still hot, but far too often it feels like it makes things harder. This session will help you fix that.
As an IT professional, you're probably in an organisation still figuring out how to implement DevOps. Maybe you have specialist DevOps roles or teams, or maybe you've just renamed some job titles. Regardless, there are fundamentals you should understand and practices you should adopt if you want DevOps to work.
This session will cover the fundamentals, why they matter, and how to effectively apply them regardless of your industry, size, or structure.
Coding in the Cloud with GitHub Codespaces
GitHub Codespaces allows you to be more productive, faster. Whether you're making your first commit to a new project, working from a tablet on the go, or coding at your desk, there's something for you.
In this session, we'll explain how Codespaces works, walk through how to get started, and show you plenty of tips and tricks to help you be super productive. Come learn how to set up your ideal dev environment in the cloud!
CI/CD and beyond with GitHub Actions
GitHub Actions is an automation tool for any software workflow related to your repository. It's fantastic for CI/CD, and can deploy almost anything to almost anywhere. But the story doesn't stop there.
In this session we'll start by looking at how you can deliver your software by building CI/CD workflows with GitHub Actions. Then, we'll dive deeper to see how you can respond to almost any event. There are dozens of events that can trigger workflows, and extensibility points for reacting to events from external tools. Come see how these can be tied together in clever ways to automate everything around your codebase.
Pragmatic DevOps - How and Why
DevOps is about delivering value to your users faster and safer. This sounds great, but it's a goal, not a guide. Let's forget the buzzwords and the tools for a moment and talk about the practical steps you can take to improve your own DevOps story. More importantly, let's talk about why DevOps is worthwhile in the first place.
Starting from basic principles and research-based literature, we'll look at practical tips for your whole process with the aim of actually delivering value to our users as effectively as we can. From taming your backlog, managing your code, and dealing with parallel workstreams, all the way to building and versioning, maintaining infrastructure, deploying safely to production, and gathering feedback to guide your next work.
Keynote: Microsoft's DevOps Transformation
“That would never work here.”
You’ve likely heard this sentiment (or maybe you’ve even said it yourself). The good news is change is possible - even in a large, traditionally slow-moving organisation.
Damian Brady explains how Microsoft's Azure DevOps team (formerly Visual Studio Team Services) went from a three-year waterfall delivery cycle to three-week iterations. How they open-sourced part of the system, and even invented and open-sourced a new Git Virtual File System to enable other teams at Microsoft to move with them.
MLOps: DevOps for Machine Learning
Machine learning is more accessible than ever before, but how do we manage the projects and collaborate with development teams? Software engineering has been maturing for decades, but introducing an unfamiliar parallel workstream like data science brings new challenges. Thankfully, we can make use of our DevOps learnings to solve some of the unique challenges.
Building effective predictive models involves data acquisition and preparation, then resource-intensive experimentation and training. Data scientists don't tend to focus on testing and deploying models to production systems in sync with software releases. Working with software engineers and IT operations in a repeatable, automated, coherent pipeline is still an afterthought in many organisations, and data science teams are left out of the loop.
Let's look at an end-to-end machine learning delivery pipeline that is repeatable and robust. We'll focus on model source control, repeatable data preparation, model training and continuous retraining, code validation and testing, model storage and versioning, and production deployment. Data scientists and software engineers can work together effectively to produce smart software. Let's learn how!
Intro to Machine Learning
What exactly is machine learning? And more importantly, will I understand the answer without doing a Masters and a PhD? Sure!
Join me, a fellow AI/ML-newbie, as I walk through what machine learning is, how it can be applied in your applications, and how you can actually create predictive models without accumulating a massive HELP debt in the process.
We'll start at the basics by talking about what machine learning can and can't do, but by the end, we'll have a working example of something actually usable. Chock full of examples and a totally non-scary amount of maths, this is the session for you if you're new to ML.
DevOps - Tips, Tricks, and Techniques
Let's forget the DevOps buzzwords and the tools and talk about practical steps you can take to improve your own DevOps story.
There are many techniques and practices that can build a good DevOps culture and process. And while every team and organisation is different, some good practices are universal.
In this session, you'll hear practical tips for improving your whole process. From taming your backlog, managing your code, and dealing with parallel workstreams, all the way to building and versioning, maintaining infrastructure, deploying safely to production, and gathering feedback.
AI for the Rest of Us
Artificial Intelligence, once science fiction, is increasingly present in our daily lives. We're seeing AI everywhere, from shopping websites to camera apps to cars. What does this mean for the software we're writing today? AI is not just for that long-shot weekend startup, but for the apps that drive our enterprises. You can add intelligence to your software more easily than you'd expect.
Teams of data scientists with PhDs and billions of dollars may lurk behind the engine showing you that scarily-prescient targeted ad. But that's changing, and AI, once exclusive and inaccessible to the average organization, is becoming available to the rest of us. Now is the time to look at adding AI to your own software, from out of the box intelligence with Azure Cognitive Services, to building and iterating on predictive models from scratch.
Let's demystify Artificial Intelligence and Machine Learning, and walk through adding useful intelligence to a business app. We'll discuss collaboration and pipelines for data scientists and software engineers in an enterprise environment, and show how you can bring smart software to production safely and repeatably. If AI intrigues you, there's no time like the present; let's get started!
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