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