I joined ActiveState last year. It’s the first time I’m working directly in the open source space. And along with learning a lot, it’s been remarkable to consider not only what the company has achieved but also the proliferation of open source software. I’ve been surprised to learn how many of us take open source for granted, especially since how it enables most of our day-to-day tech.
Can your build pipeline perfectly reproduce a build including its critical bug that is deployed to a particular subset of machines? Reproducible builds is a fundamental problem that any developer who’s shipped software has had to deal with at some point.
It seems like every time you turn around these days, there’s a new programming language trying to be the new cool kid on the block. From Rust to Swift to Go, they’re all clamouring for your attention, but what’s wrong with what you already know and love?
If you’re a Java developer like me, chances are you’ve heard rumblings of a trendy new language that came out of Google: Go.
And if, like me, you’re always looking for ways to code faster and better, you may be asking yourself whether any of your existing applications are good candidates to move to Go. While not every Java application should be ported to Go, in many cases, Go is a more productive development framework than Java. There is, therefore, a great deal of value in understanding what Go can do; where it builds on the strengths offered by Java, and where it differs.
If you visited the ActiveState booth at PyCon, QCon, GopherCon or PyData Seattle, you may have had a chance to play NeuroBlast - a simple arcade space shooter, but powered by machine learning. It was created as a demonstration of the power and accessibility of open source tools for machine learning, and uses Google’s popular TensorFlow library to drive the enemy AI in the game.
Created by Google, the Go programming language (aka golang) is an up-and-coming, modern language for building high performance, scalable applications. Our aim with ActiveGo is to make it easier for enterprises to adopt Go, and to increase the adoption of the Go language worldwide.
One of the challenges with machine learning is figuring out how to deploy trained models into production environments. After training your model, you can "freeze" the weights in place and export it to be used in a production environment, potentially deployed to any number of server instances depending on your application.
It’s becoming a common adage that every company is a tech company. As customer expectations have shifted in a “digital-first” world, companies in every industry are moving to faster development cycles, web based services, advanced data insights and even machine learning, in order to deliver greater value to customers and stay ahead of the competition.
The best way to learn something, for me at least, is to dive in head first and just do it. And while I’ve been programming for most of my life now, I’m a relative newcomer to the Go world. To truly grasp some of concepts, I wanted to take on a small, contained project that I could realistically complete.