Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications (True PDF)

Foundational Hands-On Skills for Succeeding with Real Data Science Projects

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 visualizations, 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 optimization 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 maximize development efficiency in production projects
Learn from practical Python code examples and visualizations 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 visualization 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