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ML in Production-Deploying and Scaling Machine Learning Systems

$15.99

Unleash the full potential of your machine learning projects with ML in Production: Deploying and Scaling Machine Learning Systems. This comprehensive guide takes you beyond theory, showing how to turn models into reliable, scalable systems in real-world environments. Learn the best practices for deployment, monitoring, optimization, and scaling, while avoiding common pitfalls that can derail ML applications. Perfect for data scientists, ML engineers, and tech leaders aiming to bridge the gap between experimentation and production-ready systems

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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

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