An engineer looks at that data, makes some decisions about it, and possibly takes some actions, like replacing a chiller. In the era of ML, you would start with a question “Do we need a new chiller?” Then you would give the algorithm the data set and it will tell you what to do.
As Google and many other data center operators have already found out, the ML approach to infrastructure management pays off. They are constantly adding and swapping servers with changing power, thermal, downtime risk and cost implications. DCIM is definitely an early market for the type of decision support that ML algorithms provide.
Building equipment manufacturers are also positioned to take early advantage of ML. There has been a growing trend to incorporate sensors and telematics (in other words, the IoT) into maintenance and service contracts. Collecting and sending operational data to factory technicians for remote monitoring improves preventive maintenance, helps to avoid warranty disputes, and opens the door to more flexible pay-for-performance pricing models. Applying ML algorithms to the collective data for a particular make and model AHU, for example, is a natural next step. The investment in algorithm development makes financial sense when it can optimize thousands of AHUs.
As previously stated, ML leaders will soon be getting into the Algorithms as a Service business. Pivots and advancements happen fast in the modern-day Mount Olympus settings like Google and Amazon research environments. The Artificial Intelligence (AI) unit known as Deep Mind that led the Google data center ML project has already been merged into TensorFlow, an open source software library for Machine Learning launched and curated by the Google Brain team. Resources like TensorFlow will make access to the latest AI methods and data science talent more affordable. Of course, to take advantage of these services for building optimization, you will still need to start with fully tagged building assets. Again, owner/operators that get started today on this will be among the first to be able to leverage Machine Learning to gain competitive advantage in their own businesses tomorrow.