Scaling machine learning in 3 weeks course

Scaling Machine Learning in Three Week course in a collaboration with O'Reilly following the guidance in this book.

This project contains the example code and solutions to the exercises in O'Reilly course.

Course description

Join expert Adi Polak to go in-depth into Spark, the most widely used technology in the data and machine learning ecosystem. You’ll gain hands-on experience building a scalable ML workflow with PySpark and MLflow, and you’ll learn how to deploy machine learning and reproduce your experiments.

Quick Start

Prerequisites:

• make sure you have docker installed, if not skip to the notebooks viewer.

From you local terminal, clone this repository, and enter the scaling-machine-lerning-course directory with

gh repo clone adipolak/scaling-machine-learning-course
cd scaling-machine-learning-course

From the scaling-machine-lerning-course directory, run the following docker run command:

docker run -it --memory="28g" --memory-swap="30g"  -p 8888:8888 --mount type=bind,source=$(pwd),target=/home/jovyan adipolak/ml-with-apache-spark

From here, you will see the docker run output, make sure to copy the url with the token, it would look something like this:

http://127.0.0.1:8888/?token=379fbbea91af62751b2616331d688f7a45db215b62dcfb04

Just want to quickly look at some notebooks, without executing any code?

Brows it on the Juypyter.org notebook viewer

FAQ

Is AMD/new M1/M2 supported? Not currently, in the future I will add support for those as well.

I get Py4JJavaError: An error occurred while calling o{some number}.parquet. (Reading Parquet file), what to do? From Jupyter enter the terminal and validate your Java version, given that we use Spark 3.1.1 it should be openjdk version "11.0.11". You local Java runtime should be of the same version - local Java runtime - is the Java that installed on the machine where you are running your docker from.

Which Apache Spark version should I use? The apache spark version is 3.1.1

Contributors

I want to thank everyone who contributed to this project by providing helpful feedback, filing issues, or submitting Pull Requests. If you would like to contribute, please feel free to submit a pull request, or reach out on @AdiPolak.