Implementing MLOps in the Enterprise 1st Edition

Grab the future of production-ready machine learning with Implementing MLOps in the Enterprise, 1st Edition by Yaron Haviv and Noah Gift. This authoritative guide cuts through theory to deliver actionable strategies for taking ML from prototype to scalable, reliable systems that serve real users.

Designed for engineering leaders, ML engineers, data scientists, and IT architects, the book lays out proven patterns for CI/CD for models, automated testing, model deployment, monitoring, governance, and security. You’ll find clear workflows for collaboration between data science and DevOps teams, practical checklists for production readiness, and architectural guidance for reproducible pipelines that scale across cloud and on-prem environments.

What sets this title apart is its enterprise focus: practical recipes and real-world examples tailored to the constraints of large organizations and regulated industries. Whether you’re building ML platforms in North America, Europe, Asia-Pacific, or beyond, the book equips teams to reduce technical debt, manage risk, and accelerate time-to-value.

Readers will come away with a mapped path to operational excellence—faster rollouts, robust model performance in production, and repeatable processes that support continuous innovation. Concise, hands-on, and written by experienced practitioners, this volume is a must-have reference for any organization serious about MLOps.

Ready to transform your ML initiatives into dependable business outcomes? Add Implementing MLOps in the Enterprise to your shelf and start building production-grade machine learning today.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.