Building and Scaling Machine Learning Systems
Foundations, Mathematics, and an Iterative Design Process for Production-Ready AI EngineeringBy Jonas Riedel
Length15h 5m
About this audiobook
Models are the easy part. The system around them is where projects fail.
Most machine learning books stop at the model. This one starts there and builds the production system around it. You will learn to turn a business goal into concrete latency, cost, and quality budgets, then make the data decisions that actually determine accuracy: sampling, labelling, imbalance handling, and point-in-time correct features that do not leak. The book treats machine learning engineering as a design discipline, not a collection of algorithms.
From there, you will choose baselines before architectures, train at scale with data and model parallelism, and evaluate with slices, calibration, and behavioural tests rather than a single aggregate number. Serving chapters cover batch, online, and streaming inference, tail latency, quantisation, distillation, caching, and accelerator cost. Operations chapters cover drift detection, retraining triggers, shadow deployments, and experiment design, followed by retrieval-augmented language model systems and five worked design exercises. Each chapter connects mathematical foundations to the engineering decisions they drive, so you can reason about trade-offs instead of memorising recipes.
What you will learn:
• Translate business goals into system requirements, latency budgets, and cost constraints
• Apply the mathematics that drives sampling, labelling, imbalance, and evaluation decisions
• Build leak-free feature pipelines and feature stores with point-in-time correctness
• Select baselines before architectures and train at scale with data and model parallelism
• Evaluate models with slices, calibration, and behavioural tests that predict production behaviour
• Design batch, online, and streaming serving architectures with tail-latency control
• Optimise models and infrastructure using quantisation, distillation, caching, and accelerator cost analysis
• Monitor drift, trigger retraining, run shadow deployments, and design online experiments
• Build retrieval-augmented language model systems and work through five end-to-end design exercises
Written for machine learning engineers, platform engineers, and design interview candidates who need to build and scale production-ready AI systems. If you are moving from notebooks to reliable, cost-aware, and maintainable machine learning systems, this book gives you the foundations, the mathematics, and an iterative design process to do it.
Audiobook details
GenreTechnology
Length15 hrs 5 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
Publish dateSep 27, 2026
LanguageEnglish
Table of contents
1Building and Scaling Machine Learning Systems
2Foreword
3Preface
4About This Book
5Chapter 1: From Business Goal to System Requirements
Show all chaptersShow less
61.1 The Business Objective and the Decision It Changes
71.2 From Business Metric to Model Target and Proxy
81.3 Latency, Throughput, Freshness, Quality and Cost Budgets
91.4 Is Machine Learning Warranted?
101.5 Requirements Documents, Owners and Acceptance Criteria
11Chapter 2: The Mathematics That Drives Decisions
122.1 Vectors, Matrices and the Geometry of Features
132.2 Decompositions and What They Buy You
142.3 Gradients, Backpropagation and Optimization
152.4 Probability, Likelihood and Uncertainty
162.5 Loss Functions and What They Optimize
17Chapter 3: Data Sources, Sampling and Labelling
183.1 Source Selection, Joins and Coverage
193.2 Sampling Strategies and Their Biases
203.3 Labelling Workflows, Guidelines and Agreement
213.4 Weak and Programmatic Supervision
223.5 Class Imbalance, Augmentation and Synthetic Data Limits
23Chapter 4: Feature Engineering and Feature Stores
244.1 Leakage: Forms, Detection and Prevention
254.2 Encoding, Scaling and Missing Values
264.3 Time-Aware Aggregation and Point-in-Time Correctness
274.4 Feature Store Architecture and Interfaces
284.5 Training-Serving Consistency and Parity Tests
29Chapter 5: Baselines, Model Selection and Training at Scale
305.1 Heuristic and Statistical Baselines
315.2 Capacity, Regularization and the Bias-Variance Trade
325.3 Hyperparameter Search Under a Budget
335.4 Data and Model Parallelism, Mixed Precision and Checkpointing
345.5 Reproducible Experiment Tracking
35Chapter 6: Evaluation That Predicts Production Behaviour
366.1 Offline Metric Selection and Thresholds
376.2 Calibration and Probability Quality
386.3 Slice-Based and Cohort Analysis
396.4 Threshold Selection Under Asymmetric Cost
406.5 Behavioural, Invariance and Failure-Taxonomy Tests
41Chapter 7: Pipelines, Orchestration and Reproducibility
427.1 Training and Inference Pipeline Structure
437.2 Data and Model Versioning, Lineage and Determinism
447.3 Model Registry and Release Gates
457.4 Continuous Integration for Models and Data
46Chapter 8: Serving Architectures
478.1 Batch, Online and Streaming Inference
488.2 Request Path, Feature Fetch and Latency Budgets
498.3 Autoscaling, Capacity and Cost
508.4 Fallback, Degradation and Failure Modes
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