Fisher, in the early 20th century, was a leading pioneer of modern statistics, introducing key ideas of experimental design and maximum likelihood estimation. Modern statistics as a rigorous scientific discipline traces its roots back to the late 1800s and Francis Galton and Karl Pearson. In contrast to the purely theoretical nature of probability, statistics is an applied science concerned with analysis and modeling of data. Probability theory-the mathematical foundation for statistics-was developed in the 17th to 19th centuries based on work by Thomas Bayes, Pierre-Simon Laplace, and Carl Gauss. The data gets stale and the overall system’s performance drops.Īs a discipline, statistics has mostly developed in the past century. On the other hand, a broken component can go unnoticed for some time if proper monitoring is not implemented. This makes the architecture quite robust. Moreover, if a component breaks down, the downstream components can often continue to run normally (at least for a while) by just using the last output from the broken component. This makes the system quite simple to grasp (with the help of a data flow graph), and different teams can focus on different components. Each component is fairly self-contained: the interface between components is simply the data store. Each component pulls in a large amount of data, processes it, and spits out the result in another data store, and then some time later the next component in the pipeline pulls this data and spits out its own output, and so on. Pipelines are very common in Machine Learning systems, since there is a lot of data to manipulate and many data transformations to apply.Ĭomponents typically run asynchronously. A sequence of data processing components is called a data pipeline.
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