Needless complexity in production systems is contrary to best practices and common sense. Yet quite a few developers relish beautifully crafted solutions that, in theory, can cater to everyone’s taste, but in practice serve no one. Stability and predictability are paramount to operations, yet the desire to experiment with technologies and frameworks often outweighs such considerations. To that end I’d like to present a simple principle for DevOps: 2BIG.
Almost three years ago I wrote about how I keep up with industry trends. My habits have changed quite a bit, so I thought I’d write another post about it, as it can be daunting to find pertinent information in a timely manner that is not overwhelming, especially when you are starting out in your technology career.
TensorFlow is a free, open-source machine learning framework that’s geared towards deep learning. Optimization algorithms are at the heart of artificial neural networks. We can therefore let TensorFlow solve numerical optimization problems.
Over the years I have worked with some good product managers, heard of a few great ones, and endured others who were less than stellar. From these observations and my own experience, I have distilled a set of principles for product managers, a collection of dos and don’ts.
Scio is Spotify’s open-source Scala API for Apache Beam and Google Cloud Dataflow. It’s used by data engineers at Spotify to process many petabytes of data each day. Let’s look at the different joins it supports and how and when to use each.
While you can take great stereoscopic pictures with a smartphone using sequential shots, as I described in the first and second parts of this three-part series on smartphone stereo photography, sometimes you want to capture a three-dimensional scene in motion. Even that’s possible but you do need additional gear, for instance the Kúla Bebe.