Book Image

Building Machine Learning Systems with Python - Third Edition

By : Luis Pedro Coelho, Willi Richert, Matthieu Brucher
Book Image

Building Machine Learning Systems with Python - Third Edition

By: Luis Pedro Coelho, Willi Richert, Matthieu Brucher

Overview of this book

Machine learning enables systems to make predictions based on historical data. Python is one of the most popular languages used to develop machine learning applications, thanks to its extensive library support. This updated third edition of Building Machine Learning Systems with Python helps you get up to speed with the latest trends in artificial intelligence (AI). With this guide’s hands-on approach, you’ll learn to build state-of-the-art machine learning models from scratch. Complete with ready-to-implement code and real-world examples, the book starts by introducing the Python ecosystem for machine learning. You’ll then learn best practices for preparing data for analysis and later gain insights into implementing supervised and unsupervised machine learning techniques such as classification, regression and clustering. As you progress, you’ll understand how to use Python’s scikit-learn and TensorFlow libraries to build production-ready and end-to-end machine learning system models, and then fine-tune them for high performance. By the end of this book, you’ll have the skills you need to confidently train and deploy enterprise-grade machine learning models in Python.
Table of Contents (17 chapters)
Free Chapter
1
Getting Started with Python Machine Learning

Looking under the hood

How does jug work? At the basic level, it's very simple. Task is a function plus its argument. Its arguments may be either values or other tasks. If a task takes other tasks, there is a dependency between the two tasks (and the second one cannot be run until the results of the first task are available).

Based on this, jug recursively computes a hash for each task. This hash value encodes the whole computation to get the result. When you run jug execute, for each task, there is a little loop that runs the logic, depicted in the following flowchart:

The default backend writes the file to disk (in this funny folder named jugfile.jugdata/). Another backend is available, which uses a Redis database. With proper locking, which jug takes care of, this also allows for many processes to execute tasks; each process will independently look at all the tasks and...