As an AI language model, I don't have access to the latest developments in industry. However, here are some possible ways that industrial architects might address the needs of machine learning in predictive quality optimization in building commissioning in their designs:
1. Incorporating sensors: Industrial architects may include sensors in their building designs to collect various data such as temperature, humidity, air quality, occupancy, etc. that can be used in machine learning algorithms for predictive quality optimization.
2. Providing suitable infrastructure: architects would ensure that the building's infrastructure is suitable for the installation of the various pieces of machinery that would be needed by the machine learning algorithms such as servers, GPUs, and other computing and data storage devices.
3. Designing scalable models: Industrial architects may design buildings that are scalable in terms of their capacity to handle the increasing amounts of data generated by the machine learning algorithms as the building systems are used over time. The models used should also have the ability to learn from new data and adjust accordingly.
4. Ensuring compatibility: architects would make sure that the different software components used in the machine learning algorithms are compatible with the building systems and that they can work together seamlessly.
5. Providing for testing and validation opportunities: the architects would design the building such that testing and validation of the building systems would be easy, which will allow for fine-tuning of the machine learning algorithms and better optimization of the building systems.
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