Book Image

Machine Learning Engineering with Python - Second Edition

By : Andrew P. McMahon
2.5 (2)
Book Image

Machine Learning Engineering with Python - Second Edition

2.5 (2)
By: Andrew P. McMahon

Overview of this book

The Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field. The book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized "model factory" for training and retraining of models. You'll learn to employ concepts like CI/CD and how to detect different types of drift. Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques. With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.
Table of Contents (12 chapters)
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One of our favorite libraries, good old scikit-learn, was always going to be one of the first targets for building a popular AutoML library. One of the very powerful features of auto-sklearn is that its API has been designed so that the main objects that optimize and section models and hyperparameters can be swapped seamlessly into your code.

As usual, an example will show this more clearly. In the following example, we will assume that the wine dataset (a favorite for this chapter) has already been retrieved and split into train and test samples in line with other examples, such as the one in the Detecting drift section:

  1. First, since this is a classification problem, the main thing we need to get from auto-sklearn is the autosklearn.classification object:
import numpy as np
import sklearn.datasets
import sklearn.metrics
import autosklearn.classification
  1. We must then define our auto-sklearn object. This provides several parameters that help us define how the model and...