Alpine Data An Introduction to PFA

With Chorus 6.1 we have introduced the support for PFA, the Portable Format for Analytics. Before we get into what PFA is let’s make some observations about the data science process. There are a few important questions we can ask about the process in general: 1.) What is our processing model? 2.) What are our performance characteristics? 3.) How do we refine our models? 4.) Who does the work? Training models thrives on large batches of data and, while performance is very important, (e.g. to scale), no one has expectations that the training is instantaneous. Models are often refined rapidly with a focus on experimentation. Primarily, data scientists are performing this work, lovingly cleaning their samples and elegantly attaining algorithmic excellence. When we’re scoring inputs with these models, things change.…

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