Detroit, Michigan, United States
Brian Korzynski is a Principal Architect at Iron Mountain. He has had an extensive career in Microsoft technologies working for a variety of companies both large and small. Working in many industries such as logistics, manufacturing, compliance, and finance has given him a wide variety of knowledge and skills that he wants to share with the world. Currently he is working on a variety of machine learning and artificial intelligence applications to automate manual processes.
Area of Expertise
To many people, machine learning is a black box of awesomeness that magically solves all of your problems. When you combine this with how much it is talked about in the news it can be very hard to understand what it truly is, what kind of problems it can solve, and where it fits in your developer tool belt. Starting with the basics and debunking the misconceptions we will more objectively be able analyze when and where using machine learning will fit within our projects.
Data science seems to be all the rage these days, but there are a lot of misconceptions around what this means, since everyone seems to have their own definition and there is no text book definition per say. We will touch briefly on the techniques and tools (including machine learning and big data), but focus more on the process itself and define what all is composed within data science and its related projects. Examples of these are preparing data, separating machine learning problems from programming problems, defining the process to follow, and dealing with a massive amount of variance in your data.
There has been a lot of buzz in recent years around the concept of a command bus, but if you've never used it before it can be intimidating. During this talk we will break down the basic concepts, build our own command bus, discuss pros and cons, and review different frameworks that make this work easy. By creating our own command bus we can see exactly what it does and how it works so that it will no longer be a black box. This will give you a practical introduction so that you can get started implementing this in your apps today.
Amazon AWS is the other main player in cloud computing. They have many of the same offerings as Azure, but also some that are different. We’ll take a look at the basics such as SQS, S3, EC2, SNS, and SES, to see how you can quickly and easily incorporate the cloud into your existing applications.
Thanks to Serilog, logging has never been more easy than now. We will start with the basics to get you up and running quick, then we will move to more useful things such as enrichers (custom attributes) and sinks (custom write locations). With many different sinks available you can very easy log to several different sources at one.
ML is all the rage these days, however all of the tooling seems to be only for Python developers. There are actually some good tools and frameworks that can allow you to do this kind of work within C#. We'll show you the tools and techniques by using examples of classification, regression, natural language processing, clustering, and neural networks to help do ML within C#.
Ever wonder how large companies like Apple and Netflix write applications that always just seem to work? Wonder, how we too, can write applications that can easily adapt to changing business requirements, ease the development process, and support large customer bases without a large support department? During this talk we will explore some industry best practices, hear about things that have actually worked in production, and look at examples of bad code and how to turn it into good code, so that you have a basis to work from.
Detroit, Michigan, United States