Includes admission for all talks, real time discussions, and early access to all recorded videos. View the agenda.
At the heart of Machine Learning is the model’s code, but it only accounts for a fraction of the overall code of a production system and the rest is the infrastructure needed to power and utilize it. In this workshop, we will focus on the infrastructure of a Machine Learning application written in Go and integrate the systems needed to deploy a ML model at scale.
At the 'edges' of your program (e.g. when interacting with other services) you will use serialization to send and receive data. Working efficiently with serialization will make your interfaces more flexible, will save you money on CPU & bandwidth and free you to focus on writing business value code.
Whether you are just getting started in Go or have been writing Go code for a couple years, this class will make sure you have what you need to write simple, quick, robust tests. We will start with the basics and introduce table driven tests. Then we'll work through advanced concepts such as testing asynchronous code as well as mocking and injecting code. We’ll finish up with example tests and code coverage. We believe in a hands-on approach to learning, so be ready to write a lot of tests!
This course is tailored to developers learning Go for the first time or having less than one year of Go development experience. This workshop will cover practical, daily Go fundamentals. Starting with the language fundamentals, students will move into interfaces and how they lend to embedding and composition in Go. We will finish up with an overview of concurrency in Go.
This course was designed to take experienced Go developers and walk them through how to use all the Go tooling to make your code perform better. We will start with a complete look at benchmarking from start to finish. Then a walk-through of the profiling tools will be presented. Students will finish up by learning about common optimizations in Go. After learning about these optimizations, you will be able to apply this knowledge to your own code to identify performance issues.