Hands－On.Machine.Learning.with.Scikit－Learn.and.TensorFlow含代码

Hands－On.Machine.Learning.with.Scikit－Learn.and.TensorFlow,原书及代码，今年新书，797页 A sequence of data processing components is called a data pipeline. Pipelines are very common in Machine Learning systems, since there is a lot of data to manipulate and many data transformations to apply. Components typically run asynchronously. 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 out put, and so on. Each component is fairly self-contained: the interface between components is simply the data store. This makes the system quite simple to grasp (with the help of a data flow graph), and different teams can focus on different components. 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 architecture quite robust. On the other hand, a broken component can go unnoticed for some time if proper monitoring is not implemented. The data gets stale and the overall system’s performance drops.