Overview

MLOps with Kotlin: Building Robust, Reproducible, and Automated Machine Learning Systems Machine learning projects succeed or fail not in the lab but in production. The real challenge lies in building systems that can scale, adapt, and operate reliably under real-world conditions. What if you could bring the discipline of MLOps together with the expressive power of Kotlin to create machine learning systems that are both robust and easy to maintain? This book is a hands-on guide for software engineers, data scientists, and DevOps professionals who want to master production-grade machine learning with Kotlin. You will learn how to design reproducible workflows, automate training pipelines, manage data and model artifacts, and deploy ML systems seamlessly across cloud and container environments. By combining MLOps principles with Kotlin's strengths-concise syntax, strong type safety, and JVM interoperability-you gain a practical framework for building systems that are reliable, scalable, and ready for enterprise use. What sets this book apart is its structured, end-to-end approach. You'll explore: Data Engineering Foundations: Ingestion pipelines, transformations with Kotlin DSLs, and feature engineering. Experimentation and Training: Structuring experiments, training models with TensorFlow and PyTorch interoperability, and managing datasets with version control. Automation and CI/CD: Building automated pipelines, integrating continuous training, and orchestrating workflows with Infrastructure-as-Code. Deployment Strategies: Serving models as REST or gRPC services, containerization, and scaling with Kubernetes and serverless platforms. Monitoring and Reliability: Logging, metrics, tracing, drift detection, and fault-tolerant design. Case Studies and Best Practices: Real-world examples including fraud detection and recommendation engines, plus lessons from enterprise MLOps implementations. Each chapter blends clear explanations, code illustrations, and professional insights drawn from real-world implementations. Whether you are new to MLOps or looking to refine enterprise-scale systems, you will gain actionable techniques that bridge experimentation and production without losing momentum. If your goal is to move beyond isolated models and build ML systems that last, this book will give you the blueprint. Start building reproducible, automated, and production-ready ML systems with Kotlin today.

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