Business Platform Team

Anant Corporation Blog: Our research, knowledge, thoughts, and recommendations about building and managing online business platforms.

Author Archives: Obioma Anomnachi

Machine Learning with Spark and Cassandra: Model Deployment


What is model deployment?

Model deployment is the process that we take to put our trained models to work. It involves moving our model to somewhere with the resources to do serious processing. That place also needs the ability to receive or retrieve data to be processed. We place that trained model within an architecture that delivers data to the model for processing. It then retrieves and delivers or stores the results so that they can be used or seen by users. Similar choices need to be made about whether the model gets retrained, updated, or replaced during operation.

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Cassandra Lunch #19: Combined Use of Relational Databases and Cassandra

In case you missed it, this blog post is a recap of Cassandra Lunch #19, covering the combined use of relational databases and Cassandra. We will discuss the advantages of using relational databases and Cassandra separately, before covering the advantages and methods for using both concurrently. 

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Cassandra Lunch #17: Tombstones

In Cassandra Lunch #17, we discuss tombstones in Cassandra. Tombstones are a special kind of write that signifies deleted values, stops them from being returned on reads, and eventually allows them to be deleted during compaction. We discuss what tombstones are and why they are used, as well as how they work in practice.

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Spark and Cassandra For Machine Learning: Model Selection Tests

Model-selection tests are used to determine which of the two trained machine learning models performs better. The point of model selection tests is to predict which model will generalize better to unseen data and thus comparisons of single test results are not enough. Today we will run through a number of different model selection tests, discuss how they work and how we interpret their results.

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Spark and Cassandra For Machine Learning: Cross-Validation

Cross-validation is a collection of methods for repeated training and testing of our machine learning models. We do it in order to learn more than simple testing can tell us. These tests can help us tune our model parameters. We need to do this before any final evaluation takes place and we try to move forwards to deployment.

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