scaling-machine-learning-course

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scaling-machine-learning-course

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Scaling Machine Learning in Three Week course in a collaboration with O'Reilly following the guidance of Adi Polak's book - Scaling Machine Learning with Spark

scaling-machine-learning-course 최신버젼 다운로드

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// repository documentation

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# 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](https://amzn.to/3WgHQvd).

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

```code
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:

```code
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:

```code
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](https://nbviewer.org/github/adipolak/scaling-machine-learning-course/tree/main/notebooks/)
<img src="https://nbviewer.jupyter.org/static/img/nav_logo.svg" width="90" />

# 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](https://twitter.com/AdiPolak).

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