Название: Software Engineering for Data Scientists (MEAP v3)Автор: Andrew Treadway
Издательство: Manning Publications
Год: 2023
Страниц: 319
Язык: английский
Формат: pdf, epub
Размер: 16.8 MB
These easy to learn and apply software engineering techniques will radically improve collaboration, scaling, and deployment in your data science projects.
In Software Engineering for Data Scientists you’ll learn to improve performance and efficiency by:
Using source control
Handling exceptions and errors in your code
Improving the design of your tools and applications
Scaling code to handle large data efficiently
Testing model and data processing code before deployment
Scheduling a model to run automatically
Packaging Python code into reusable libraries
Generating automated reports for monitoring a model in production
Software Engineering for Data Scientists presents important software engineering principles that will radically improve the performance and efficiency of data science projects. Author and Meta data scientist Andrew Treadway has spent over a decade guiding models and pipelines to production. This practical handbook is full of his sage advice that will change the way you structure your code, monitor model performance, and work effectively with the software engineering teams.
The book is split into four parts:
• Part 1 – Getting started
This part will cover topics such as source control, exception handling, better structuring your code, object-oriented programming (OOP) for data science, and monitoring the progress of your code (such as model training or data extraction)
• Part 2 – Scaling
Part 2 covers scaling your code effectively. For example – how do you deal with larger datasets? We’ll cover both the computational and memory components of scaling
• Part 3 – Scheduling, testing, and deployment into production
Part 3 details how to rigorously test your code, protecting your credentials (for example when connecting to a database to query data, scheduling models and data pipelines to run automatically, and packaging data analytics code into a portable library that can be shared with and downloaded by others
• Part 4 – Monitoring your data processing and modeling code
Lastly, Part 4 will teach you how to effectively monitor your code in production. This is especially relevant when you deploy a machine learning model to make predictions on a recurring or automated basis. We’ll cover logging, automated reporting, and how to build dashboards with Python.
In addition to the direct topics we cover in the book, you’ll also get hands-on experience with the code examples. The code examples in the book are meant to be runnable on your own with downloadable datasets, and you’ll find corresponding files available in the Github repository. Besides the examples laid out in the book, you’ll also find Practice on your own sections at the end of most chapters so that you can delve further into the material in a practical way.
about the technology
Many basic software engineering skills apply directly to Data Science! As a data scientist, learning the right software engineering techniques can save you a world of time and frustration. Source control simplifies sharing, tracking, and backing up code. Testing helps reduce future errors in your models or pipelines. Exception handling automatically responds to unexpected events as they crop up. Using established engineering conventions makes it easy to collaborate with software developers. This book teaches you to handle these situations and more in your Data Science projects.
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