Subject
16 entries
Book
Bookmarks
Data Engineering Design Patterns (DEDP)
Data Engineering Design Patterns (DEDP) is a free online book covering canonical patterns for building data pipelines — ingestion, transformation, storage, and orchestration. Structured as a pattern catalog rather than a tutorial, useful as a reference for recurring architectural decisions.
Graph-Powered Machine Learning
Alessandro Negro's Manning book covers the intersection of graph theory and machine learning, from knowledge graphs and GNNs to fraud detection and recommendations using Neo4j. It's a practical end-to-end treatment that bridges graph databases and ML for practitioners.
Kubernetes for MLOps: Scaling Enterprise Machine Learning, Deep Learning, and AI
A book/transcript from the This Week in ML podcast by Sam Charrington on using Kubernetes as the operational backbone for enterprise ML workloads. Covers the full spectrum from containerized training jobs to model serving, making the case that Kubernetes is the de facto standard for scaling ML in production.
Effective Data Science Infrastructure
Ville Tuulos's Manning book on building productive data science infrastructure, with Metaflow as its centerpiece framework. The core argument—that infrastructure exists to make people productive, not to be technically clever—is a useful corrective to the endless tooling churn in ML engineering.
Designing Cloud Data Platforms
Manning textbook on building modern cloud data platforms covering ingestion, storage, processing, and serving layers across major providers. Practical guide for data engineers designing end-to-end pipelines that balance performance, cost, and operational complexity.
Build a Career in Data Science
A practical book by Jacqueline Nolis and Emily Robinson on navigating a data science career, from landing your first job through managing teams and handling workplace politics. More grounded than most career books because both authors have actually worked as practicing data scientists.
Advanced Algorithms and Data Structures
Marcello La Rocca's Manning textbook on advanced algorithms and data structures, organized around practical problems like caching, nearest-neighbor search, clustering, and graph planarity. A useful reference for engineers who have outgrown intro-level algorithms and need principled solutions to real design challenges.
High Assurance Rust
High Assurance Rust is a free online book teaching systems security through Rust — covering memory safety, type systems, cryptography, and formal verification. Aimed at developers who want to write software that's provably hard to exploit.
Introduction to Machine Learning Interviews Book
Chip Huyen's free ML interviews book — covers both the process of landing ML roles and the technical depth (math, coding, ML concepts) that hiring loops test. Dual-purpose: career strategy and technical review.
Portable Python Projects: Home Automation on Raspberry Pi
A Pragmatic Programmers book teaching home automation with Raspberry Pi and Python — projects like smart lighting, temperature monitoring, and voice control, most completable in an hour. Bridges the gap between Python programming and physical home control without requiring electronics expertise.
Interpretable Machine Learning
Christoph Molnar's free online book covering the theory and practice of interpretable machine learning — from inherently interpretable models (decision trees, linear regression) to post-hoc methods (SHAP, LIME, counterfactuals). The standard reference for understanding and explaining ML model behavior.
Regression and Other Stories
Regression and Other Stories by Gelman, Hill, and Vehtari is a practical statistics textbook covering regression modeling from basics through causal inference — grounded in real data examples and the Bayesian workflow. The modern standard for applied regression.
Introduction to Probability for Data Science
Introduction to Probability for Data Science by Stanley Chan is a free undergraduate textbook covering probability theory through regression and hypothesis testing, with code examples in Python, R, MATLAB, and Julia. Designed specifically for the data science curriculum rather than pure math.
Models and Algorithms for Unlabelled Data
Vaibhav Verdhan's Manning book on unsupervised learning algorithms, covering clustering, dimensionality reduction, and anomaly detection with Python implementations on real-world datasets. Practical in orientation — stronger on applied case studies than mathematical rigor.
An Introduction to Programming in Go
Caleb Doxsey's free online book introducing the Go programming language — a clean, concise introduction covering types, functions, concurrency primitives, and the standard library. Go had just turned four years old in 2014 and was attracting serious attention for systems and network programming.
Distributed Systems for Fun and Profit
Mixu's free online book on distributed systems fundamentals — covering consistency models, CAP theorem, replication, and consensus — written for practitioners who want theoretical grounding without the academic overhead. One of the clearest introductions to the field that exists.