Four short links: 25 September 2018
Software Engineering, ML Hardware Trends, Time Series, and Eng Team Playbooks
- Notes to Myself on Software Engineering — Code isn’t just meant to be executed. Code is also a means of communication across a team, a way to describe to others the solution to a problem. Readable code is not a nice-to-have; it is a fundamental part of what writing code is about. A solid list of advice/lessons learned.
- Machine Learning Shifts More Work To FPGAs, SoCs — compute power used for AI/ML is doubling every 3.5 months. FPGAs and ASICs are already predicted to be 25% of the market for machine learning accelerators in 2018. Why? FPGAs and ASICs use far less power than GPUs, CPUs, or even the 75 watts per hour Google’s TPU burns under heavy load. […] They can also deliver a performance boost in specific functions chosen by customers that can be changed along with a change in programming.
- Time Series Forecasting — one of those “three surprising things” articles. The three surprising things: You need to retrain your model every time you want to generate a new prediction; sometimes you have to do away with train/test splits; and the uncertainty of the forecast is just as important as, or even more so, than the forecast itself.
- Health Monitor — Atlassian’s measures of whether your team is doing well. Their whole set of playbooks is great reading for engineering managers.
