hands-on lab
PySpark - How to build a Machine Learning Pipeline
Difficulty: Beginner
Duration: Up to 1 hour
Students: 396
Rating: 4.4/5
Get guided in a real environmentPractice with a step-by-step scenario in a real, provisioned environment.
Learn and validateUse validations to check your solutions every step of the way.
See resultsTrack your knowledge and monitor your progress.
Description
In this hands-on lab, you will master your knowledge of PySpark, a very popular Python library for big data analysis and modeling. Here, you will learn how to create a machine learning pipeline using the PySpark library, and to perform metric evaluation and model tuning.
Your machine learning skills will be challenged, and by the end of this lab, you should have a deep understanding of how PySpark practically works to build data analysis pipelines.
Learning Objectives
Upon completion of this lab you will be able to:-
fit a Logistic Regression model in PySpark;
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perform cross-validation in PySPark;
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evaluate the model performances;
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perform inference on new, unseen data.
Intended Audience
This lab is intended for:
- Those interested in performing data analysis with Python.
- Anyone involved in data science and engineering pipelines.
Prerequisites
You should possess:
- An intermediate understanding of Python.
- Basic knowledge of SQL.
- Basic knowledge of the following libraries: Pandas.
Updates
May 9th, 2023 - Updated instructions to address logging in
Covered topics
Lab steps
PySpark - Machine Learning Pipeline