About this book:

This book provides a comprehensive, practitioner‑focused guide to building reliable, scalable, and maintainable machine learning systems in production. It begins by establishing the MLOps mindset—applying DevOps principles while addressing the unique challenges of data, models, and continuous experimentation—and then walks through the full ML lifecycle: problem scoping, data governance and versioning, batch and streaming data pipelines, feature stores for reusable assets, experiment tracking for reproducibility, and model registries for versioned artifacts. Each chapter emphasizes automation, testing (data, model, and system), and shift‑left practices to catch issues early, with concrete templates, checklists, and playbooks that teams can adapt to their own stacks.

The core operational flow continues with CI/CD for ML (automated builds, validation, conditional retraining, and deployment), deployment patterns such as Blue/Green, Canary, and Shadow releases, and model serving strategies for real‑time APIs, batch jobs, and edge devices. Orchestration and workflow engines (Airflow, Prefect, Dagster, Kubeflow Pipelines) are detailed to manage complex pipelines, while monitoring and observability chapters cover data quality, drift, model performance, logs, metrics, and traces. Alerting, SLOs, and incident response are treated as first‑class concerns, alongside security, privacy, compliance, cost management, scalability, and reliability engineering. Human‑in‑the‑loop feedback and continuous learning mechanisms are highlighted as essential for adapting models to changing data and maintaining trust, and a dedicated chapter on Responsible AI outlines fairness, bias mitigation, transparency, and governance practices.

Finally, the book shows how to operationalize these principles at scale: cross‑functional workflows clarified with RACI matrices, reusable templates and playbooks for project scoping, model development, production readiness, retraining, and incident response, and illustrative case studies from industries such as streaming personalization, fraud detection, and predictive maintenance. It concludes with guidance on team topologies (enabling, platform, stream‑aligned with embedded expertise) and building an MLOps platform roadmap that aligns technology investments with business value, ensuring that ML systems evolve from experimental notebooks to dependable, production‑grade assets that deliver continuous, safe, and measurable impact.

What You'll Find Inside:

• Scoping ML products: Defining clear business objectives, user needs, and operational requirements (latency, throughput, data freshness) to ensure ML solutions solve real problems.
• Data management and governance: Implementing data quality checks, versioning, lineage, and feature stores to treat data as a first-class citizen and ensure consistency between training and serving.
• Reproducibility and tracking: Using experiment tracking, model versioning, and registries to capture the full lineage of data, code, and models for reliable debugging and auditing.
• Automated ML lifecycle: Building CI/CD pipelines for automated builds, validation, and deployment, employing patterns like blue/green, canary, and shadow for safe production rollouts.
• Production observability: Monitoring data quality, model drift, and performance, combined with logging, metrics, and tracing, to detect issues early and enable rapid incident response.

Who's It For:

This book is intended for data scientists, ML engineers, software engineers, platform teams, SREs, product managers, and leaders responsible for ML outcomes. It assumes readers have experience training models and basic familiarity with modern tooling, but need guidance to build reliable, scalable, and maintainable ML systems in production—whether starting from scratch or enhancing existing operations.