Spencer Schneidenbach
Spencer Schneidenbach is a consultant, Microsoft MVP, and speaker in the St. Louis, Missouri area.
Full speaking history and blog can be found at http://schneids.net.
A Deep Dive on Running Background Tasks in .NET Core
When it comes to running background tasks in .NET Core, there are a bunch of choices available, ranging from the built-in and straightforward (BackgroundService) to more fully-featured NuGet packages (like HangFire) - but knowing which one is appropriate for your use case is not always obvious. Does your task need to run on a timer? Is it short-lived or long-running? What if your task fails? How do you best enable recovery for tasks that fail?
In this session, Spencer will break down the multitude of options for running background tasks in .NET Core. We'll discuss their advantages, disadvantages, use cases, ease of use, development experience, and more. By the end of the session, you'll have the information you need to make an informed decision about how you want to run your background tasks in .NET Core.
Behind the Streams: An In-Depth Look At ML- and AI-Powered Analysis of FPS Gameplay
Streaming is one of best forms of entertainment for gamers, casual and hardcore alike – and one of Spencer’s favorite games to watch is Escape From Tarkov. However, while streams can be fun to watch, they often last several hours, and not everyone has the time to sit through hours of gameplay to catch the most thrilling moments.
In this session, we'll dive into how Spencer used machine learning, coding, media processing tools, and a dash of AI to identify individual games within 8+ hour streams – from knowing the map being played on to the kill count within the game to knowing exactly when the most intense moments occurred. We'll explore how this data is analyzed, timestamped, stored, and ultimately used to cut the raw stream into individual, watchable segments of gameplay.
This engaging session will spotlight a series of technologies that was used to ultimately solve the problem: how can I watch the best games, on my favorite maps, for my favorite streamer, LVNDMARK? If you're a developer interested in machine learning, AI, data processing, or media analysis, this session will leave you with insights, inspiration, and a new perspective on the powerful tools we developers have available to us.
Spencer Schneidenbach is a consultant, Microsoft MVP, and speaker in the St. Louis, Missouri area.
Full speaking history and blog can be found at http://schneids.net.
A Deep Dive on Running Background Tasks in .NET Core
When it comes to running background tasks in .NET Core, there are a bunch of choices available, ranging from the built-in and straightforward (BackgroundService) to more fully-featured NuGet packages (like HangFire) - but knowing which one is appropriate for your use case is not always obvious. Does your task need to run on a timer? Is it short-lived or long-running? What if your task fails? How do you best enable recovery for tasks that fail?
In this session, Spencer will break down the multitude of options for running background tasks in .NET Core. We'll discuss their advantages, disadvantages, use cases, ease of use, development experience, and more. By the end of the session, you'll have the information you need to make an informed decision about how you want to run your background tasks in .NET Core.
Behind the Streams: An In-Depth Look At ML- and AI-Powered Analysis of FPS Gameplay
Streaming is one of best forms of entertainment for gamers, casual and hardcore alike – and one of Spencer’s favorite games to watch is Escape From Tarkov. However, while streams can be fun to watch, they often last several hours, and not everyone has the time to sit through hours of gameplay to catch the most thrilling moments.
In this session, we'll dive into how Spencer used machine learning, coding, media processing tools, and a dash of AI to identify individual games within 8+ hour streams – from knowing the map being played on to the kill count within the game to knowing exactly when the most intense moments occurred. We'll explore how this data is analyzed, timestamped, stored, and ultimately used to cut the raw stream into individual, watchable segments of gameplay.
This engaging session will spotlight a series of technologies that was used to ultimately solve the problem: how can I watch the best games, on my favorite maps, for my favorite streamer, LVNDMARK? If you're a developer interested in machine learning, AI, data processing, or media analysis, this session will leave you with insights, inspiration, and a new perspective on the powerful tools we developers have available to us.