Side Quests: How creative tinkering and exploration can transform you, one side project at a time

If you're anything like me, you like to build fun things and have fun while doing so. In this fun talk we'll talk about the value of side projects as a tool for perpetual learning, growth, emotional health, and career development in the tech world.

We'll tour the landscape of side projects and why you might want to pick one up, how to select a side project and get started with one, how to constrain the scope of a side project and make progress against it, and how to bring side projects to their inevitable conclusion.

Side projects aren't easy, though, and so we'll talk about getting unstuck, finding time for side projects, what to do when you're bored of a side project (or tempted by a new one), and ways of growing and staying healthy in those seasons of life when side projects just aren't feasible.

Along the way, you'll hear some tales of interesting, innovative, amusing, and even horrifying side projects I've taken on over the years and hear a few specific ways that side projects have impacted myself and those I've had the privilege of mentoring.

Common Architectures for Software, Data, and AI Systems

Let's face it; software systems have changed a lot over the last few decades. In this introductory talk we'll survey common architectures for software, AI, and data processing systems and discuss the concerns and tradeoffs that go into selecting a candidate systems architecture.

We'll cover traditional N-Tier application architectures as well as more recent innovations such event-driven and serverless architectures, microservices, modular monoliths and more. We'll also look at various data ingestion and processing pipelines and talk about the role of MLOps and Data Engineering in enterprise organizations. We'll close by tying everything together and seeing how AI systems can be deployed to orchestrate different data sources and external capabilities.

By the time we're done you'll have a solid understanding of the different ways of structuring modern software, AI, and data systems and when you might choose one approach over another.

Dungeons & Debugging: Observability, Testing, and Analytics battle AI Complexity

Artificial intelligence and machine learning can significantly level up your software capabilities, offering exciting possibilities for applications like interactive storytelling and game mastering in Dungeons and Dragons. However, as any adventurer knows, these powerful technologies can also introduce challenges akin to facing a rogue dragon—unexpected behavior and complex troubleshooting. In this talk, we’ll explore how to harness the benefits of AI/ML solutions while maintaining control and minimizing unpredictability. We’ll use observability through OpenTelemetry as our magic map to gain insights into the labyrinth of AI orchestration systems, whether through frameworks like Semantic Kernel or custom telemetry collection. Additionally, we will examine how features like aggregate and individual feature importance in machine learning can help demystify and manage these AI systems, much like identifying the strengths and weaknesses of a mythical creature.

Furthermore, this session will cover the application of automated testing to AI and machine learning systems—a bit like ensuring your thief can disarm traps before the party walks into an ambush. We'll discuss the implementation of unit and integration tests for both LLM-based systems and traditional machine learning approaches. The role of data analytics will also be highlighted, emphasizing data-driven decision-making by analyzing usage patterns, processing time, confidence scores, user behavior, and more. Finally, we'll share strategies for taming unruly AI/ML systems in controlled and safe ways, illustrated with engaging examples from a Dungeons and Dragons AI system alongside anonymized real-world cases. By the end of this comprehensive session, you'll be equipped with the knowledge to control your AI solutions effectively and tackle AI-related challenges with the confidence of a seasoned adventurer, all while having a bit of fun along the way.

Roll for Intelligence: Crafting an AI Dungeon Master with Semantic Kernel and RAG

Are you a fan of Dungeons and Dragons or curious about how AI can blend with creative storytelling? Imagine a world where scheduling conflicts no longer derail your adventures, and your Dungeon Master is available anytime. In this engaging talk, you'll discover how the creativity and exploration of D&D inspired the development of an AI-powered Dungeon Master. We’ll examine the progression of AI systems, starting with a basic AI game master utilizing prompt engineering, and then address its limitations. By grounding the system in data using retrieval augmentation generation (RAG) and tools like Semantic Kernel, we can guide the AI to make more informed and consistent decisions. We will also explore how integrating search and memory capabilities with various data sources like table storage, relational databases, data files, and vector databases ensures that responses to player input remain true to the game world, rules, and characters. Additionally, we'll discuss token usage and managing complex, long-term, multi-session interactions with an AI system.

By the end of this session, you'll have a broad understanding of AI system complexities and high-level operations, along with specific implementation details from an AI game master project using Semantic Kernel, OpenAI, and various storage providers. We will also delve into how agentic AI systems can modularize complex interactions, resist decay as they're scaled, and add layers of defense to maintain quality responses and engaging sessions. This entertaining talk aims to inspire your own project ideas and provide strategies for constraining, controlling, and expanding AI systems in the future. Whether you're a seasoned player, an AI enthusiast, or someone who enjoys unique, nerdy projects, you'll leave with new insights into how technology and imagination can work together to keep the dice rolling!

Mad Data Science

Artificial intelligence and machine learning are awesome and powerful things that let us have things like self-driving cars, medical scanners, and even help get cereal boxes to your local supermarket on time. But AI doesn't have to just be for adults and it certainly doesn't have to be used for serious business.

In this session we'll interact with AI and machine learning and see how they work on a trial and error basis. We'll see reinforcement learning in action as we train a digital squirrel to find an acorn and get safely back home to its tree. We'll see artificial intelligence at work as we interact with a talking dog that can hear us, see us, and respond to us in its own voice - and see how changing a few settings results in a completely different experience. We'll also see how machine learning can help predict how good or bad a movie or TV show will be - based on the choices we make together.

The session will be good for a laugh, but before its done you'll learn the basics of artificial intelligence, machine learning, and more.

Identifying, Analyzing, Communicating, and Resolving Technical Debt

Our software sucks. We're up to our necks in bugs and technical debt, yet we often seem to hit roadblocks explaining things in ways that bring about meaningful change. In this session you'll learn to gather, analyze, and interpret data in order to create effective presentations to communicate quality, technical debt, and other technical matters in ways that tell a compelling story. You’ll master how to communicate effectively with key stakeholders by taking a data-driven approach blended with storytelling techniques to bridge common gaps between development and business stakeholders. We'll cover various architectural and analytical processes that can support the decision-making process, justify paying down technical debt, tips for reducing its accumulation in the future, and focusing on improving our software in the areas that need it most.

This talk was tremendously well-received at CodeMash, StirTrek, and as an online course at Pluralsight for some time. Some of the topics of this talk found their way into my first technical book, Refactoring with C#, though that book is more focused on tactical execution in a specific language.

Refactoring with C#

Technical debt and legacy code suck to deal with. Let's take a look at how modern C# and Visual Studio can help make it better.

In this talk we'll cover the built-in code analysis and refactoring tools in Visual Studio, ways of codifying and enforcing code standards in the editor through .editorconfig files and custom Roslyn Analyzers that can provide team-specific code analysis results and refactorings. We'll also discuss how new tools like GitHub CoPilot can assist in analyzing, refactoring, documenting, and even testing your code as you refactor.

Finally, we'll close with a discussion of refactoring code in enterprise organizations and agile environments including getting buy-in for paying down technical debt and rolling out changes in a safe and repeatable manner.

AI Fairness, Transparency, Privacy, and Accessibility

"AI is perfectly safe and has no bias, risks, or safety concerns whatsoever", said nobody ever.

As AI-based systems become more pervasive it's important to remember that these systems are for everyone and should benefit everyone. Sadly, this isn't always the case. Our very data is often biased heavily against different ages, genders, and ethnicities in ways that can be hard to detect.

Additionally, AI systems themselves can often be a new source of data from chat transcripts, user content, and more. How this data is treated matters greatly because even anonymized data may not be as anonymized as we think it is.

Finally, even when AI systems are working properly, they may be used in ways we don't intend or approve of, including ways that might cause real harm to others.

In this talk we'll talk about various ethical, privacy, safety, and fairness concerns in AI systems (generative and otherwise) and how to detect and remediate these issues so our AI systems can truly be for everyone.

Building the Bat Computer with Semantic Kernel, .NET, and OpenAI

Are you a budding super hero or super villain* but you feel like you're lacking that AI companion to help you reach that next level? Have you ever wanted to see what AI can do to help your daily life? Do you have an interesting application that would just be so much better with a little artificial intelligence? Don't fear, because of course there's a PreCompiler for that.

In this half-day workshop we'll take a starter C# application and progressively expand it by adding speech, vision, language, and decision-making capabilities with Azure AI services, plus a healthy sprinkling of large language models and OpenAI.

By the time we're done, you'll have a better understanding of what AI can do and see how easy it is to integrate it into your applications. You'll also be that much closer to super hero / super villain* status with a modular personal AI companion of your very own to continue extending after CodeMash is over.

* CodeMash does not endorse acts of villainy including but not limited to taking over the world and/or the Kalahari.

Beginning Machine Learning in C# with ML.NET AutoML

Did you know you can train and use machine learning inside of your .NET applications without needing detailed knowledge of machine learning algorithms? In this talk we'll explore the ML.NET AutoML API capabilities and how accessible machine learning in C# really is as we write C# code to solve machine learning problems.

We'll focus heavily on the automatic algorithm selection features of AutoML in ML.NET and talk about the various tasks it can achieve before drilling deeper to apply AutoML to solve a multi-class classification problem. We'll train a machine learning model and have it predict video game ESRB ratings for a few hypothetical games provided by the audience, then host this new model in a .NET 6 minimal Web API project in ASP .NET Core.

We'll also explore evaluating model performance, ideal training times, and how trained models can be saved and loaded for use in production applications, as well as some places you can go to learn more about ML.NET and machine learning in general.

NOTE: This is different than the AutoML CLI or Model Builder. Our core focus is on C# code for Machine Learning using the AutoML as baby steps into Machine Learning

This talk is a mixture of slides and code.

This talk can be performed in person without an internet connection

Automating my Dog with Azure AI

Like many terrier owners, I have a problem. My dog is overworked from the constant need to monitor multiple streets to bark at squirrels or passers by. I'd like to free up more of his time and energy for snuggling and play but the outdoors must still be monitored. Thankfully, it turns out that much of what my dog does can be automated using Azure AI Services and Azure OpenAI.

In this talk we'll use this absurd premise to build an AI dog through the Azure AI Vision, Azure AI Speech, and Azure AI Language APIs as well as Azure OpenAI. We'll explore how these different capabilities can work in tandem to create an immersive application that is able to respond semi-intelligently to what the user says and incorporate that with its knowledge, sight, and personality.

By the end of this session you'll have more of an understanding of what Azure AI Services and Azure OpenAI can do. You'll also learn the basics of how to interact with them from code so that you, like my dog, can take advantage of pre-trained machine learning models to enhance devote more of your energy to other areas.

This will require an internet connection. It is also completely ridiculous.

Rock Paper Scissors and other absurd things you can do with Computer Vision

Computer vision is an interesting and almost magical field. In this talk we'll explore the different forms of computer vision, their uses in everyday applications, and common libraries and languages that help with them. By the time we're done you'll have an understanding of what computer vision is, how it works at a high level using deep learning, and how you can get started with it using either OpenCV, PyTorch, or an API-based approach like Azure Cognitive Services.

Expanding Your .NET Testing Toolbox

Ever wanted to get better at unit testing in .NET but didn't know where to start? In this session we'll go over a variety of tools and libraries that can take your .NET and .NET Core unit tests to the next level.

We'll take a fictitious legacy codebase and add basic tests to it, then diversify into new areas such as mocking, fluent assertions, randomized data, API testing, UI testing, parameterized tests, snapshot tests, behavior driven development, and other techniques.

You'll leave with an even larger bag of tricks and the knowledge that whatever the nature of the application you're developing, you have the tools to test it.

Aimed at new and intermediate .NET developers with an early understanding of unit tests in .NET.

Managing your AI / ML Pricing on Azure

Machine learning and artificial intelligence are awesome, and Azure makes them affordable and scalable, but there's still some things you should know to understand and plan for your pricing needs using machine learning and AI on Azure.

Let's take a look at the pricing components of Azure Machine Learning, Azure Cognitive Services, and the newer OpenAI offerings that are now part of Azure as well. We'll discuss some common costs you can anticipate and potentially mitigate and using Azure Cost Management to discover the services costing you the most money each month.

Intro to Application Architecture and Scalability

Software is a lot more complicated than it used to be. Twenty years ago most applications lived on individual machines as executables. Today modern applications run in a combination of environments including web applications, mobile apps, APIs, and databases.

In this talk we'll take a look at the various components that comprise modern web applications, explore performance and scalability, and give a general overview of advanced architectural concepts like caching, database partitioning, NoSQL, microservices, and containerization.

By the end of this talk you should have a greater high-level understanding of modern architectural concepts and how the pieces fit together, as well as more knowledge on where and when these approaches are appropriate to use.

Presented at a variety of user groups and aimed at the introductory level, but touching on more advanced concepts.

AI for Everyone?

Artificial intelligence has been promising to revolutionize the world since before the personal computer even existed. But just like that, AI suddenly seems to actually be changing the world. From self-driving cars to LLMs to GitHub Copilot, AI is causing real and sudden changes in the world around us.

But is this AI future we find ourselves in really for everyone? How ethical and safe are these systems? What even is AI anyway, and how can you get started with it?

In this talk we'll cover the breadth of the AI landscape, common AI tasks, how AI is changing in recent years, ethical concerns in AI, and how and where you personally can get started with AI.

Stand Back; I'm going to try Data Science!

Curious about data science and its relation to software engineering? Want to know how to dabble in artificial intelligence or machine learning side projects before taking the plunge? Come check out this session.

In this session I'll highlight my own journey in layering data science skills on top of a software engineering background. I'll teach you the terms, roles, languages, libraries, and technologies you'll encounter and help you understand what aspects of math and programming are helpful in setting down this journey.

You'll discover easy ways to get started with Python, R, and get connected to the data science community. I'll show you how to discover public datasets and visualizations to help inspire your own journey. By the time the session is finished, you'll know how to find out if data science is a good fit for you and how to take it to the next level if you discover you like it.

Is Die Hard a Christmas Movie? Let's ask Azure!

When it comes to popular Christmas movies there's a recurring debate as to whether or not that list of movies should include the 1988 film Die Hard. Thankfully, machine learning has been applied to this problem and we have an answer.

We'll start off by using Python, Pandas, and Jupyter Notebooks to analyze and clean movie data and then prepare it for model training while avoiding factors that might introduce bias.

After that we'll explore using the beginner-friendly features of Azure Machine Learning Studio's Automated ML to train and evaluate a classification model.

Finally, we'll deploy the trained model to Azure as a web service and see what it has to say about Die Hard.

By the time we're done you'll know the truth about Die Hard and have a deeper understanding of machine learning experiments and some common beginner-friendly tools involved in machine learning.

Interactive Notebooks for the Polyglot with Python, .NET, SQL, JS, and more!

Have you ever wanted to run a small experiment or create an example to share with your team but didn't want to go through the boilerplate of setting up a new project? Did you ever wish you could embed interactive code examples alongside your documentation? Project Jupyter and Polyglot Notebooks might be up your alley.

In this talk you'll see how Jupyter Notebooks, Jupyter Labs, and Polyglot Notebooks (formerly .NET Interactive) offer interactive ways of running code in a minimal environment alongside rich markup documentation. We'll cover what notebooks are, what they're used for, setting up your notebook in Jupyter and Polyglot Notebooks, and how the various code blocks can be used.

Along the way we'll feature examples using Python, JavaScript, C#, F#, SQL, HTML, Mermaid diagrams, and more.

Finally, we'll close with a discussion of sharing your notebooks with others and how notebooks can serve as rich interactive documentation, proofs of concept, and might even be considered an entire class of application in their own right.

The first bit of this will cover Jupyter Notebooks and Jupyter Labs. Anaconda individual edition will be discussed as a good way of getting started and cloud-based providers for notebooks will also be discussed.

The second half of this talk will feature Polyglot Notebooks (see https://devblogs.microsoft.com/dotnet/dotnet-interactive-notebooks-is-now-polyglot-notebooks/ ) and show how notebooks can be used for more than just Python development or data science experiments.

Exploratory Data Analysis in Python with Pandas and Plotly

Let's take a look at how Python can be an effective platform for analyzing and visualizing data.

In this talk we'll use Jupyter Notebooks, Pandas, and Plotly to visualize several datasets to find trends in our data.

We'll cover loading and transforming data in Pandas and NumPy, including cleaning missing values and performing feature engineering. We'll also look into the built-in aggregation and statistical techniques in Pandas DataFrames.

Next we'll take our data and see how Plotly can generate compelling scatter plots, box plots, histograms, tree maps, and more with just a bit of code.

By the time we're done you'll see why I often prefer data visualization in Python over dedicated tools like Tableau or Power BI.