In December, SolarWinds software engineers from all over the world will meet in Brno. We are taking the occasion to invite you to three public workshops focused on up-to-date technology and trends.
The workshops run simultaneously, you can thus attend only one of them.
In this workshop, attendees will get hands-on with Arduino hardware and the TinyGo project. TinyGo is an alternative compiler for the Go programming languages, targeted at ARM architectures and the very limited resources available on microcontrollers. Attendees will work with live hardware and the Arduino board’s interactions with different components like LEDs. First 20 participants receive a free Arduino Nano.
Presenter: Trevor Rosen
Trevor Rosen is a principal architect in the Application Management group at SolarWinds. Over the course of his career, he’s worked as a developer or architect in industries as varied as advertising, politics, healthcare, network management, information security, and point-of-sale. He’s long been active in the open-source community, running meetups and contributing to a wide range of projects, most recently in Go. His current professional focus points lie in microservice application architecture and cloud-native operations.
This workshop will introduce Bayesian statistics and the probabilistic perspective on machine learning. This will lead to a demonstration of a probabilistic programming treatment of a machine learning problem using Jupyter notebooks. This talk is intended as a technical session for those with an interest in statistics and data science. Probabilistic programming is a relatively obscure and emergent field in machine learning and not many people have come across it yet.
Presenter: Alex Chan
Alex joined SolarWinds in Edinburgh last year as a data scientist after completing an M.S. in artificial intelligence. He is interested in the fields of deep learning, Gaussian processes, and probabilistic machine learning.
This workshop will be focused on comparing the current release of ML.NET and the different available anomaly detection methods for numeric time series. The presenter will show how to set up ML.NET to analyze numeric time series data and evaluate which method is best for a given use case. Evaluation criteria for evaluating the accuracy of anomaly detection algorithms will be shown so that as ML.NET adds more support for new algorithms, students will be able to apply the criteria to those new approaches.
Presenter: Dan Jagnow
Dan Jagnow is a senior architect at SolarWinds, where he focuses on platform architecture. Previously, he was a consultant at Cobb Systems, where he completed projects for Dell, the Texas Education Agency, TXU Energy, and other organizations. Dan specializes in the Microsoft .NET platform, but has worked with a broad variety of technologies and platforms over the course of his career. He received B.S. degrees in computer science and mathematics from Texas Tech University and an M.S. in computer science from Northwestern University.