Machine Learning in Production is a crash course in data science and machine learning for people who need to solve real-world problems in production environments. Written for technically competent "accidental data scientists" with more curiosity and ambition than formal training, this complete and rigorous introduction stresses practice, not theory. Building on agile principles, Andrew and Adam Kelleher show how to quickly deliver significant value in production, resisting overhyped tools and unnecessary complexity.
Master Best Practices for Managing Technical Debt to Promote Software Quality and Productivity
As software systems mature, earlier design or code decisions made in the context of budget or schedule constraints increasingly impede evolution and innovation. This phenomenon is called technical debt, and practical solutions exist. In Managing Technical Debt, leading experts Philippe Kruchten, Robert Nord, and Ipek Ozkaya introduce integrated, empirically developed principles and practices that any software professional can use to gain control of technical debt in any software system.
The Object-Oriented Thought Process
Written by Matt Weisfeld, a developer for developers who want to improve their understanding of object-oriented technologies, The Object-Oriented Thought Process provides a solutions-oriented approach to object-oriented programming. This revised edition focuses on the OOP technologies that have survived the past 20 years and remain at its core, with new and expanded coverage of design patterns, avoiding dependencies, and the SOLID principles to help make software designs understandable, flexible, and maintainable.