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By Borevich Z. I., Oskolkov A. P., E. V. Podsypanin

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We will focus most of our attention in this book on the model training, evaluation, and cross-validation steps for various machine learning techniques, using MLlib and Spark’s core features. Model deployment and integration Once we have found the optimal model based on the train-test loop, we might still face the task of deploying the model to a production system so that it can be used to make actionable predictions. Usually, this process involves exporting the trained model to a central data store from where the production-serving system can obtain the latest version.

First, we mapped our records of (user, product, price) to the records of (product, 1). Then, we performed a reduceByKey operation, where we summed up the 1s for each unique product. Once we have this transformed RDD, which contains the number of purchases for each product, we will call collect, which returns the results of the computation to the driver program as a local Scala collection. We will then sort these counts locally (note that in practice, if the amount of data is large, we will perform the sorting in parallel, usually with a Spark operation such as sortByKey).

However, we must ensure that the machine learning systems designed on Spark also meet these criteria. There is no good in implementing an algorithm that ends up having bottlenecks that cause our system to fail in terms of one or more of these requirements. Designing a Machine Learning System Introducing MovieStream To better illustrate the design of our architecture, we will introduce a practical scenario. Let’s assume that we have just been appointed to head the data science team of MovieStream, a fictitious Internet business that streams movies and television shows to its users.

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A. V. Malyshev, Scientist And Teacher by Borevich Z. I., Oskolkov A. P., E. V. Podsypanin


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