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Tag Archives: cassandra


Machine Learning with Spark and Cassandra: Model Deployment

Introduction

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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Open Source Notebooks and Cassandra: Doing SQL on Cassandra Tables

In this blog post, we will introduce a few open-source notebooks that we can use to do SQL on Cassandra. At the bottom of the blog, we have an accompanying webinar that you can watch to see a live demo using 2 of the notebooks we discuss in this blog. This is Part 3 of our series on “Doing SQL and Reporting on Apache Cassandra with Open Source Tools”, and Parts 1 and 2 are also linked below. Also, be on the lookout for part 4 coming soon!

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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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