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Pragmatic Programmer for Machine Learning

Marco Scutari | Mauro Malvestio
€59.99
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A01=Marco Scutari
A01=Mauro Malvestio
Apache Spark
Author_Marco Scutari
Author_Mauro Malvestio
Category=GPH
Category=UMZ
Category=UYQM
computational statistics
Cpu Cache
Cpu Core
data engineering
DBSCAN
DBSCAN Cluster Algorithm
Deep Neural Network Models
Deep Neural Networks
Distributed Version Control Systems
end-to-end machine learning pipelines
eq_bestseller
eq_computing
eq_isMigrated=1
eq_nobargain
eq_non-fiction
Floating Point Variables
Glue Code
Hyperparameter Tuning
Jupyter Notebooks
Machine Learning
Machine Learning Framework
Machine Learning Models
Machine Learning Software
Machine Learning Systems
Natural Language Processing
Non-zero Cells
pipeline architecture
probabilistic modelling
reproducible research
Software Defined Networking
software testing methods
System Ram
Technical Debt
Version Control
Version Control Systems
Vice Versa

Product details

  • ISBN 9780367255060
  • Weight: 560g
  • Dimensions: 156 x 234mm
  • Publication Date: 13 Apr 2025
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Description

Machine learning has redefined the way we work with data and is increasingly becoming an indispensable part of everyday life. The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions discusses how modern software engineering practices are part of this revolution both conceptually and in practical applictions.

Comprising a broad overview of how to design machine learning pipelines as well as the state-of-the-art tools we use to make them, this book provides a multi-disciplinary view of how traditional software engineering can be adapted to and integrated with the workflows of domain experts and probabilistic models.

From choosing the right hardware to designing effective pipelines architectures and adopting software development best practices, this guide will appeal to machine learning and data science specialists, whilst also laying out key high-level principlesin a way that is approachable for students of computer science and aspiring programmers.
About Marco Scutari | Mauro Malvestio

Marco Scutari is a Senior Researcher at Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), Switzerland. He has held positions in statistics, statistical genetics and machine learning in the UK and Switzerland since completing his PhD in statistics in 2011. His research focuses on the theory of Bayesian networks and their applications to biological and clinical data, as well as statistical computing and software engineering.

Mauro Malvestio is a senior technologist based in Milan, Italy, with more than 15 years of experience in software engineering and IT operations in consulting and product companies as a CTO. His research focuses on software engineering, machine learning systems, embedded systems and cloud computing.

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