A containerized approach using: Apache Kafka, Spark, Cassandra, Hive, Postgresql, Jupyter, and Docker-compose.

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Extract features are one of the essential processes in machine learning pipelines. Unfortunately, when the data volume grows fast, perform repetitive operations in ETL pipelines becomes expensive. A simple solution for this problem is to build a feature store, where you can store features to reuse in different machine learning projects. This post’s objective is to propose a guide on building a feature store for studies purpose or deployment.

You can check more about Feature Stores here. …


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In this post, we will cover how to build a simple machine learning application for sentiment analysis. Our focus here is to classify the comments of a specific Instagram post.

I assume you have a basic knowledge of programming on Python and the libraries Flask, scikit-learn, and NLTK. If you want to jump to code immediately, take a look at my GitHub here.

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1-Download the data

The choice of the database is a critical task. The words present in the Instagram posts must be well represented in the data set we use to train our model. …

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