Cannot agree more with this paper from Google: ‘One of the basic arguments in this paper is that machine learning packages have all the basic code complexity issues as normal code, but also have a larger system-level complexity that can create hidden debt. Thus, refactoring these libraries, adding better unit tests, and associated activity is time well spent but does not necessarily address debt at a systems level. In this paper, we focus on the system-level interaction between machine learning code and larger systems as an area where hidden technical debt may rapidly accumulate. At a system-level, a machine learning model may subtly erode abstraction boundaries. It may be tempting to re-use input signals in ways that create unintended tight coupling of otherwise disjoint systems. Machine learning packages may often be treated as black boxes, resulting in large masses of “glue code” or calibration layers that can lock in assumptions. Changes in the external world may make models or input signals change behavior in unintended ways, ratcheting up maintenance cost and the burden of any debt. Even monitoring that the system as a whole is operating as intended may be difficult without careful design. Indeed, a remarkable portion of real-world “machine learning” work is devoted to tackling issues of this form. Paying down technical debt may initially appear less glamorous than research results usually reported in academic ML conferences. But it is critical for long-term system health and enables algorithmic advances and other cutting-edge improvements.’ (via Grady Booch)
Regarding smart home power management — Niall Douglas on ITC says “If you choose your solar inverter components right, they’ll come with a LAN capable mains AC meter which you stick just after the mains. It essentially duplicates the smart meter, should get very close, but it’s on your LAN and you can Home Assistant script the lot. My notes here suggest [this meter] for €385 inc VAT delivered, it talks to all the other Fronius kit such as inverter and thermal store immersions over your LAN. All with high quality Home Assistant support.”
The Forecast.Solar service provides solar production forecasting for your solar panel system, based on historic averages combined with weather forecasting. This integration provides an estimated forecast on how much energy your solar panels are going to produce, allowing you to plan ahead on how you spend your produced energy in most efficiently.