As generative AI technologies become more mainstream, it's a good time to explore some of the other aspects that make up AI.
experience an attack at some point in time. That said, there are still steps that businesses should take to minimize the effects of an attack, including implementing sound security policies and processes as well as creating governance frameworks to minimize risk.The sheer volume of data that AI applications and processes require means that general-purpose databases often turn out to be both inadequate and expensive, inhibiting scalability.
For example, factors such as temperature, humidity, light levels, speed, velocity and pressure, along with corresponding time stamps, all contribute to a comprehensive understanding of a system's dynamics. By accurately instrumenting and monitoring a system, engineers can collect data to observe its behavior. They can then analyze that data to determine what happened—providing a source of truth when trying to find out how a system reacted at any given point in time.
However, effectively managing all of the data needed to optimize automation is a challenge that requires specialized infrastructure, and a critical component of this tech stack is time series databases. The sheer volume and velocity of time series measurements, as seen in examples ranging from self-driving cars to space-bound rockets, necessitate high ingest rates, efficient data compression and fast query times.
As AI capabilities continue to evolve, the automation landscape will grow exponentially, transforming industries and unlocking new possibilities for the future.
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