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Machine Learning Infrastructure and Best Practices for Software Engineers

You're reading from  Machine Learning Infrastructure and Best Practices for Software Engineers

Product type Book
Published in Jan 2024
Publisher Packt
ISBN-13 9781837634064
Pages 346 pages
Edition 1st Edition
Languages
Author (1):
Miroslaw Staron Miroslaw Staron
Profile icon Miroslaw Staron

Table of Contents (24) Chapters

Preface 1. Part 1:Machine Learning Landscape in Software Engineering
2. Machine Learning Compared to Traditional Software 3. Elements of a Machine Learning System 4. Data in Software Systems – Text, Images, Code, and Their Annotations 5. Data Acquisition, Data Quality, and Noise 6. Quantifying and Improving Data Properties 7. Part 2: Data Acquisition and Management
8. Processing Data in Machine Learning Systems 9. Feature Engineering for Numerical and Image Data 10. Feature Engineering for Natural Language Data 11. Part 3: Design and Development of ML Systems
12. Types of Machine Learning Systems – Feature-Based and Raw Data-Based (Deep Learning) 13. Training and Evaluating Classical Machine Learning Systems and Neural Networks 14. Training and Evaluation of Advanced ML Algorithms – GPT and Autoencoders 15. Designing Machine Learning Pipelines (MLOps) and Their Testing 16. Designing and Implementing Large-Scale, Robust ML Software 17. Part 4: Ethical Aspects of Data Management and ML System Development
18. Ethics in Data Acquisition and Management 19. Ethics in Machine Learning Systems 20. Integrating ML Systems in Ecosystems 21. Summary and Where to Go Next 22. Index 23. Other Books You May Enjoy

Text data

For the text data, we’ll use the same Hugging Face hub to obtain two kinds of data – unstructured text, as we did in Chapter 3, and structured data – programming language code:

# import Hugging Face Dataset
from datasets import load_dataset
# load the dataset with text classification labels
dataset = load_dataset('imdb')

The preceding code fragment loads the dataset of movie reviews from the Internet Movie Database (IMDb). We can get an example of the data by using an interface that’s similar to what we used for images:

# show the first example
dataset['train'][0]

We can visualize it using a similar one too:

# plot the distribution of the labels
sns.histplot(dataset['train']['label'], bins=2)

The preceding code fragment creates the following diagram, showing that both positive and negative comments are perfectly balanced:

Figure 6.13 – Balanced classes in the IMDb movie database reviews

Figure 6.13 – Balanced classes in the...

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