Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications(Addison-Wesley Data & Analytics Series)

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. Drawing on their extensive experience, they help you ask useful questions and then execute production projects from start to finish. The authors show just how much information you can glean with straightforward queries, aggregations, and visualisations, and they teach indispensable error analysis methods to avoid costly mistakes. They turn to workhorse machine learning techniques such as linear regression, classification, clustering, and Bayesian inference, helping you choose the right algorithm for each production problem. Their concluding section on hardware, infrastructure, and distributed systems offers unique and invaluable guidance on optimisation in production environments. Andrew and Adam always focus on what matters in production: solving the problems that offer the highest return on investment, using the simplest, lowest-risk approaches that work. Leverage agile principles to maximise development efficiency in production projects Learn from practical Python code examples and visualisations that bring essential algorithmic concepts to life Start with simple heuristics and improve them as your data pipeline matures Avoid bad conclusions by implementing foundational error analysis techniques Communicate your results with basic data visualisation techniques Master basic machine learning techniques, starting with linear regression and random forests Perform classification and clustering on both vector and graph data Learn the basics of graphical models and Bayesian inference Understand correlation and causation in machine learning models Explore overfitting, model capacity, and other advanced machine learning techniques Make informed architectural decisions about storage, data transfer, computation, and communication

Table of Contents:
Part I: Principles of Framing Chapter 1: The Role of the Data Scientist Chapter 2: Project Workflow Chapter 3: Quantifying Error Chapter 4: Data Encoding and Preprocessing Chapter 5: Hypothesis Testing Chapter 6: Data Visualization Part II: Algorithms and Architectures Chapter 7: Introduction to Algorithms and Architectures Chapter 8: Comparison Chapter 9: Regression Chapter 10: Classification and Clustering Chapter 11: Bayesian Networks Chapter 12: Dimensional Reduction and Latent Variable Models Chapter 13: Causal Inference Chapter 14: Advanced Machine Learning Part III: Bottlenecks and Optimizations Chapter 15: Hardware Fundamentals Chapter 16: Software Fundamentals Chapter 17: Software Architecture Chapter 18: The CAP Theorem Chapter 19: Logical Network Topological Nodes Bibliography

About the Author :
Michael Freeman, senior lecturer at the University of Washington Information School, teaches data science, interactive data visualization, and web development. Previously, he was data visualization specialist and research fellow at the Institute for Health Metrics and Evaluation. Joel Ross, senior lecturer at the University of Washington Information School, teaches web and mobile development, software architecture, and introductory programming; and researches gamification, pervasive systems, computer science education, and social computing.