ML in Production: Deploying and Scaling Machine Learning Systems is a hands-on guide for turning machine learning prototypes into robust, scalable, and reliable real-world applications. Whether you’re a data scientist, ML engineer, or tech leader, this eBook equips you with the tools and strategies needed to move from experimentation to deployment with confidence.
Discover best practices for model deployment, monitoring, versioning, CI/CD pipelines, infrastructure management, and scaling ML systems in cloud and edge environments. Learn how to tackle real-world challenges like data drift, model decay, and operational bottlenecks — all while maintaining performance and reliability.
If you’re ready to take your machine learning projects beyond the lab and into production, this eBook is your roadmap to success










